Predicting the Price of Semiconductor Stocks
Chris Dare
2024-05-10
Introduction
With the growing presence of technology in society, there is a rapidly increasing demand for hardware which supports our computational needs. One of the most important pieces of hardware for computationally-intensive workloads are Graphics Processing Units (GPUs) due to their ability to handle a multitude of parallel processing tasks; as a result, they have become an invaluable resource for companies pursuing artificial intelligence, scientific computing, cryptocurrency mining, computer graphics, or any other processing-heavy task. For example, companies on the forefront of the technology industry (such as Meta, Amazon, Apple) are a blue chip in any investor’s stock portfolio; however, it is important to recognize that each of these companies depends on the ability to process vast amounts of users’ data every day. Consequently, companies which manufacture processing chips have been a focal point for investors in recent years, as they directly control the output capabilities for a large portion of the technology sector.
The purpose of this project is to attempt to predict the price-trends of a semiconductor stock - in fact, there are four stocks we wish to simultaneously predict: Nvidia, Advanced Micro Devices (AMD), Intel Corporation, and Taiwan Semiconductor Manufacturing. Though tackling four separate stocks slightly deviates from the goal of applying various of machine learning algorithms to a single data-set, the hope is that this will give a better prediction of which machine learning models are most effective for stock market predictions (at least in the case of semiconductor stocks). Put differently, if we found that a particular statistical learning model was the best fit a some stock, this would naturally raise the question of whether that is due to a characteristic of the model or a characteristic of the stock. Therefore, for each of the four chip manufacturers listed above, we will apply a variety of statistical learning models, ranging from standard regression to more non-linear models like random forest and k-Nearest neighbors.
Loading Packages and Data
knitr::opts_chunk$set(echo = TRUE)
library(tidyverse)
library(tidymodels)
library(ggplot2)
library(corrplot)
library(discrim)
library(ggthemes)
library(kableExtra)
library(yardstick)
library(visdat)
library(scales)
library(glmnet)
library(quantmod)
library(dplyr)
library(dygraphs)
library(tidyr)
library(ranger)
library(xgboost)
library(vip)
library(swipeR)
library(htmltools)
library(plotly)
library(base64enc)
tidymodels_prefer()
conflicted::conflicts_prefer(yardstick::rsq)
conflicted::conflicts_prefer(dplyr::lag)
set.seed(3435)One of the most useful packages for the analysis of stock market data
in R is the quantmod package. While there are several
built-in functions in the quantmod library which already
predict stock market trends using statistical learning, we will simply
use this package for the purpose of pulling live stock data from Yahoo Finance and visualizing the
data in a much neater fashion than usual ggplot2 (as
certain statistical-learning models in the quantmod package
are not highly compatible with tools we will need later in this
analysis).
To expand slightly more on the application of this library, the
quantmod package allows us to circumvent the process of
downloading a CSV file of stock data and reading it into R by instead
calling the loadSymbols() function — this method then
automatically loads all stock data (over a potentially specified
time-frame) into a data structure assigned the same name as the stock’s
symbol. Since the function loadSymbols will automatically
set the most recent day to pull stock data as the current day, we must
specify a fixed “last day” to ensure that our models run the same data
every time; in particular, we will fix our time-frame as the 2023 fiscal
year, ranging from January \(1^{\text{st}}\), 2023 to January \(1^{\text{st}}\), 2024. It is worth noting
that the table loaded by loadSymbols is
not a data-frame; its original datatype must be used
for other functions in the quantmod package such as the
chartSeries plots applied below, but we will later need to
typecast this object to a usual data-frame in order to apply most of the
tidyverse functions.
Nota Bene: By default, any call to the
loadSymbols function will overwrite data stored in a
variable with the same name as the stock symbol (e.g. AMD, NVDA, INTC).
Therefore, it is highly encouraged to either (1) rename the tables as
soon as possible (i.e. AMD_2, AMD_table) to ensure any changes aren’t
lost in a successive call to loadSymbols or (2) explicitly
set auto.assign = FALSE in all calls to
loadSymbols
Below are formatted plots of the market data for our four stocks
(AMD, NVDA, INTC, and TSM) obtained using the chartSeries()
function in quantmod. We will postpone the discussion of
what several of the terms in each mean until the Exploratory Data
Analysis section below.
AMD <-loadSymbols("AMD", from="2023-01-01", to="2024-01-01", auto.assign = FALSE)
chartSeries(AMD,TA=c(addVo(),addBBands(),addMACD()))
my_dates <- index(AMD)
# Typecast as dataframe object
AMD <- data.frame(AMD)
# Rename variables to remove redundancy
AMD <- AMD %>% rename(
Close = AMD.Close,
High = AMD.High,
Low = AMD.Low,
Open = AMD.Open,
Volume = AMD.Volume,
Adjusted = AMD.Adjusted
)
AMD$Date <- my_datesNVDA <- loadSymbols("NVDA", from="2023-01-01", to="2024-01-01", auto.assign = FALSE)
chartSeries(NVDA,TA=c(addVo(),addBBands(),addMACD()))
# Typecast as dataframe object
NVDA <- data.frame(NVDA)
# Rename variables to remove redundancy
NVDA <- NVDA %>% rename(
Close = NVDA.Close,
High = NVDA.High,
Low = NVDA.Low,
Open = NVDA.Open,
Volume = NVDA.Volume,
Adjusted = NVDA.Adjusted
)
NVDA$Date <- my_datesINTC <- loadSymbols("INTC", from="2023-01-01", to="2024-01-01", auto.assign=FALSE)
chartSeries(INTC,TA=c(addVo(),addBBands(),addMACD()))
# Typecast as dataframe
INTC <- data.frame(INTC)
# Rename variables to remove redundancy
INTC <- INTC %>% rename(
Close = INTC.Close,
High = INTC.High,
Low = INTC.Low,
Open = INTC.Open,
Volume = INTC.Volume,
Adjusted = INTC.Adjusted
)
INTC$Date <- my_datesa<-loadSymbols("TSM", from="2023-01-01", to="2024-01-01")
chartSeries(TSM,TA=c(addVo(),addBBands(),addMACD()))
# Typecast as dataframe
TSM <- data.frame(TSM)
# Rename variables to remove redundancy
TSM <- TSM %>% rename(
Close = TSM.Close,
High = TSM.High,
Low = TSM.Low,
Open = TSM.Open,
Volume = TSM.Volume,
Adjusted = TSM.Adjusted
)
TSM$Date <- my_datesWith each stock’s data loaded and typecast into a data-frame, we also
want to examine the total number of predictors and observations for each
individual stock. As the beginning and ending dates specified in
loadSymbols are the same across our four stocks, it
suffices to check the dimensions of only one of our data frames:
dim(AMD)## [1] 250 7One may be initially misled by the fact that only 250 observations appear across a years worth of data; i.e. we only have 250 out of 365 days. However, this is simply a reflection of the fact that the New York Stock Exchange does not operate on weekends and a hanful of holidays. In fact, a quick analysis shows that there is no missing data among any of the four stocks — this is somewhat expected, however, since stock market data is meant to be publicly available and the original features are fairly common metrics for financial institutions to collect.
g1 <- vis_miss(AMD) + labs(x = "Missing Data for AMD")
g2 <- vis_miss(NVDA) + labs(x = "Missing Data for NVDA")
g3 <- vis_miss(INTC) + labs(x = "Missing Data for INTC")
g4 <- vis_miss(TSM) + labs(x = "Missing Data for TSM")
wrapper <- swipeRwrapper(
tags$div(style = "width: 500px; margin: auto;", ggplotly(g1)),
tags$div(style = "width: 500px; margin: auto;", ggplotly(g2)),
tags$div(style = "width: 500px; margin: auto;", ggplotly(g3)),
tags$div(style = "width: 500px; margin: auto;", ggplotly(g4))
)
swipeR(wrapper, height = "400px", navigationColor = "navy")Exploratory Data Analysis
With our four stocks pulled and loaded into data frames, we first wish to examine the relevance of each predictor initially provided and then examine other possible ways to analyse the performance of a given stock. As measuring the performance of a financial security over time relies not only on historical data, but historical data whose relevancy may depreciate depending on how recent it is (or isn’t), much of the motivation behind our added predictors in the latter half of this section revolves around study of time-series in statistics. However, since the focus of this project is on statistical-learning models and how they are applied to certain problems, several important techniques in the study of time-series (and thus stock-market analysis) will not be applied within this project.
Describing the Predictors
In the first half of this section, we examine the initial predictors
loaded into the the data frames for our stocks. After removing some of
the redundancy of the variables names in the previous section using the
dplyr library’s rename() function, we wind up
with the following variables for each of the four stocks (AMD, NVDA,
INTC, and TSM):
Open: The opening price of the stock that day (specifically, the value of the stock at 9:30AM EST).Close: The closing price of the stock that day (specifically, the value of the stock at 4:00PM EST)Low: The minimum valuation of the stock over the given day.High: The maximum valuation of the stock over the given day.Volume: The total number of shares traded on a stock over the given day (i.e. both bought and sold).Adjusted: The closing price of the stock that day together with any adjustments due to corporate actions such as rights offerings, dividends, and splits.
The dygraph package provides a compact and convenient
way for us to simultaneously visualize and interact with several of the
above predictors for each stock:
p <- dygraph(AMD[,c(1:4)], xlab = "Date", ylab = "Price", main = "AMD Price") %>%
dySeries("Open", label = "Open", color = "black") %>%
dySeries("Low", label = "Low", color = "red") %>%
dySeries("High", label = "High", color = "green") %>%
dySeries("Close", label = "Close", color = "orange") %>%
dyRangeSelector() %>%
dyCandlestick()%>%
dyCrosshair(direction = "vertical") %>%
dyHighlight(highlightCircleSize = 3, highlightSeriesBackgroundAlpha = 0.2, hideOnMouseOut = T) %>%
dyRoller(rollPeriod = 1)
pBy dragging one’s mouse over a region of the graph or adjusting the date slider at the bottom of each plot, individual data points may be more closely examined.
p <- dygraph(NVDA[,c(1:4)], xlab = "Date", ylab = "Price", main = "NVDA Price") %>%
dySeries("Open", label = "Open", color = "black") %>%
dySeries("Low", label = "Low", color = "red") %>%
dySeries("High", label = "High", color = "green") %>%
dySeries("Close", label = "Close", color = "orange") %>%
dyRangeSelector() %>%
dyCandlestick()%>%
dyCrosshair(direction = "vertical") %>%
dyHighlight(highlightCircleSize = 3, highlightSeriesBackgroundAlpha = 0.2, hideOnMouseOut = T) %>%
dyRoller(rollPeriod = 1)
pp <- dygraph(INTC[,c(1:4)], xlab = "Date", ylab = "Price", main = "INTC Price") %>%
dySeries("Open", label = "Open", color = "black") %>%
dySeries("Low", label = "Low", color = "red") %>%
dySeries("High", label = "High", color = "green") %>%
dySeries("Close", label = "Close", color = "orange") %>%
dyRangeSelector() %>%
dyCandlestick()%>%
dyCrosshair(direction = "vertical") %>%
dyHighlight(highlightCircleSize = 3, highlightSeriesBackgroundAlpha = 0.2, hideOnMouseOut = T) %>%
dyRoller(rollPeriod = 1)
pp <- dygraph(TSM[,c(1:4)], xlab = "Date", ylab = "Price", main = "TSM Price") %>%
dySeries("Open", label = "Open", color = "black") %>%
dySeries("Low", label = "Low", color = "red") %>%
dySeries("High", label = "High", color = "green") %>%
dySeries("Close", label = "Close", color = "orange") %>%
dyRangeSelector() %>%
dyCandlestick()%>%
dyCrosshair(direction = "vertical") %>%
dyHighlight(highlightCircleSize = 3, highlightSeriesBackgroundAlpha = 0.2, hideOnMouseOut = T) %>%
dyRoller(rollPeriod = 1)
pAdded Predictors and Metrics
While the six predictors pulled from Yahoo Finance give significant insight into each stock’s historical performance over the year, there may be other, more useful ways that we can use to assess the future growth of our securities. For example, we are attempting to predict data which relies on it’s past/recent behavior; however, all of the variables in our data frame are aligned to measure the same 24-hour window. This naturally leads to the study of time series in statistics, which will motivate the remaining variables that our algorithms will depend on.
Lag Values
As mentioned above, the analysis of stock market data is a problem which falls into the branch of statistics known as time-series. Ultimately, the study of time-series is a vast and rich subject that we cannot hope to even begin uncovering within the scope of this project; however, we will attempt to provide rudimentary explanations of the necessary topics which we incorporate for the sake of completeness.
Foremost, a time series is a collection of observations \(\{ X_t : t \in T \}\) gathered over some time domain \(T \subseteq [0, \infty)\) and ordered chronologically; while it is not necessarily intrinsic of general time series, many examples have the property that the values depend on previous historical data. For these types of examples, whenever we are given sample of data points \(\{ X_1, X_2, \dots, X_n \}\) ordered chronologically, we refer to \(X_{i-1}, X_{i-2}, \dots, X_{i-p}\) as the lag variables associated to \(X_i\). Conveniently, this allows us to model \(X_i\) as a function of its lag variables; for example, autoregression refers to any regression-based techniques applied to the lag variables (and potentially other predictors) of a time-series.
As many of the computations in R are done via vector operations
(i.e. on the columns of a data frame), defining a lag variable is
effectively synonymous to shifting the values in a given vector “down”.
While this would likely require some sort of for-loop
operation in other languages, R provides the useful lag
method to do exactly this. However, there is one shortcoming that arises
regardless of the language used: how do we define the lag variable for
the first observation? In R, since we are shifting a given vector down
by \(p\) entries, the first \(p-1\) entries of the lag variable are
filled in with NAm which can potentially cause issues for
numerical computations down the road. One common fix for this is to
simply pretend the model is constant prior to the first observation, and
define the lag variables \(X_{1 - p} =
X_1\) — this can be done in R using the fill
command, which uses the next closest value so that statistical
properties like mean and standard deviation are better preserved.
However, as this is effectively equivalent to assuming the data is
constant prior to the first observation, it is worth mentioning that
there are more advanced techniques which account for the initial trend
of the data.
# Define lag variables for AMD
AMD$Close_L1 <- lag(AMD$Close, 1)
AMD$Close_L2 <- lag(AMD$Close, 2)
AMD$Close_L3 <- lag(AMD$Close, 3)
AMD$Close_L4 <- lag(AMD$Close, 4)
AMD$Close_L5 <- lag(AMD$Close, 5)
AMD$Close_L6 <- lag(AMD$Close, 6)
AMD$Close_L7 <- lag(AMD$Close, 7)
AMD$Close_L8 <- lag(AMD$Close, 8)
AMD$Close_L9 <- lag(AMD$Close, 9)
AMD$Close_L10 <- lag(AMD$Close, 10)
# Fill in NA entries in beginning of lag variables
AMD <- AMD %>%
fill(Close_L1, .direction = "up") %>%
fill(Close_L2, .direction = "up") %>%
fill(Close_L3, .direction = "up") %>%
fill(Close_L4, .direction = "up") %>%
fill(Close_L5, .direction = "up") %>%
fill(Close_L6, .direction = "up") %>%
fill(Close_L7, .direction = "up") %>%
fill(Close_L8, .direction = "up") %>%
fill(Close_L9, .direction = "up") %>%
fill(Close_L10, .direction = "up")
# Define lag variables for NVDA
NVDA$Close_L1 <- lag(NVDA$Close, 1)
NVDA$Close_L2 <- lag(NVDA$Close, 2)
NVDA$Close_L3 <- lag(NVDA$Close, 3)
NVDA$Close_L4 <- lag(NVDA$Close, 4)
NVDA$Close_L5 <- lag(NVDA$Close, 5)
NVDA$Close_L6 <- lag(NVDA$Close, 6)
NVDA$Close_L7 <- lag(NVDA$Close, 7)
NVDA$Close_L8 <- lag(NVDA$Close, 8)
NVDA$Close_L9 <- lag(NVDA$Close, 9)
NVDA$Close_L10 <- lag(NVDA$Close, 10)
# Fill in NA entries
NVDA <- NVDA %>%
fill(Close_L1, .direction = "up") %>%
fill(Close_L2, .direction = "up") %>%
fill(Close_L3, .direction = "up") %>%
fill(Close_L4, .direction = "up") %>%
fill(Close_L5, .direction = "up") %>%
fill(Close_L6, .direction = "up") %>%
fill(Close_L7, .direction = "up") %>%
fill(Close_L8, .direction = "up") %>%
fill(Close_L9, .direction = "up") %>%
fill(Close_L10, .direction = "up")
# Define lag variables for INTC
INTC$Close_L1 <- lag(INTC$Close, 1)
INTC$Close_L2 <- lag(INTC$Close, 2)
INTC$Close_L3 <- lag(INTC$Close, 3)
INTC$Close_L4 <- lag(INTC$Close, 4)
INTC$Close_L5 <- lag(INTC$Close, 5)
INTC$Close_L6 <- lag(INTC$Close, 6)
INTC$Close_L7 <- lag(INTC$Close, 7)
INTC$Close_L8 <- lag(INTC$Close, 8)
INTC$Close_L9 <- lag(INTC$Close, 9)
INTC$Close_L10 <- lag(INTC$Close, 10)
# Fill in NA entries
INTC <- INTC %>%
fill(Close_L1, .direction = "up") %>%
fill(Close_L2, .direction = "up") %>%
fill(Close_L3, .direction = "up") %>%
fill(Close_L4, .direction = "up") %>%
fill(Close_L5, .direction = "up") %>%
fill(Close_L6, .direction = "up") %>%
fill(Close_L7, .direction = "up") %>%
fill(Close_L8, .direction = "up") %>%
fill(Close_L9, .direction = "up") %>%
fill(Close_L10, .direction = "up")
# Define lag variables for TSM
TSM$Close_L1 <- lag(TSM$Close, 1)
TSM$Close_L2 <- lag(TSM$Close, 2)
TSM$Close_L3 <- lag(TSM$Close, 3)
TSM$Close_L4 <- lag(TSM$Close, 4)
TSM$Close_L5 <- lag(TSM$Close, 5)
TSM$Close_L6 <- lag(TSM$Close, 6)
TSM$Close_L7 <- lag(TSM$Close, 7)
TSM$Close_L8 <- lag(TSM$Close, 8)
TSM$Close_L9 <- lag(TSM$Close, 9)
TSM$Close_L10 <- lag(TSM$Close, 10)
# Fill in NA entries
TSM <- TSM %>%
fill(Close_L1, .direction = "up") %>%
fill(Close_L2, .direction = "up") %>%
fill(Close_L3, .direction = "up") %>%
fill(Close_L4, .direction = "up") %>%
fill(Close_L5, .direction = "up") %>%
fill(Close_L6, .direction = "up") %>%
fill(Close_L7, .direction = "up") %>%
fill(Close_L8, .direction = "up") %>%
fill(Close_L9, .direction = "up") %>%
fill(Close_L10, .direction = "up") Any time one is utilizing lag variables in a time series problem, a natural question which arises is “how far back should we look to measure current data”? In other words, what is the optimal lag length \(p\) for a given time series. It turns out this an incredibly involved problem that does not have a single universal answer for all time-series based questions; there are several advanced techniques guided by economic theory for auto-regressive models such as the F-test approach and Bayes information criterion (BIC) / Akaike information criterion (AIC) that are often used, but we will unfortunately omit any discussion of these techniques since the focus of this project is on the statistical learning models themselves. Instead, we will naively take the lag length to be two weeks (i.e. 10 business days) which is fairly short for economic time series problems measured on a daily scale.
Simple Moving Average of Closing Price
One issue that immediately arises after defining lag variables is that they are almost always highly correlated with one another. This makes sense, as they are in fact measuring the same continuous observable; however, this may lead to a significant amount of multicollinearity within our model. One way to eliminate this issue is to instead measure the average of our lag variables across a potentially different lag length, known as the simple moving average (SMA). Specifically, we define \[ \operatorname{SMA}(X_i, k) = \frac{1}{k}\sum_{j=1}^{k}X_{i-j} \] Notice that the above formula is non-inclusive of the current data point — this will turn out to be and incredibly important detail for our regression-based models to be of any use, as otherwise they can simply recover the desired outcome directly. For example, suppose we wanted to train an autoregressive linear model based off of the past 5 days’ lag variables and the 5 day simple moving average. That is, we wish to find coefficients \(\beta_0, \beta_1, \dots, \beta_5, \beta_{\text{SMA}}\) to minimize the residual sum of squares in \[ X_i = \beta_0 + \beta_1 X_{i-1} + \beta_2 X_{i-2} + \beta_3 X_{i-3} + \beta_4 X_{i-4} + \beta_5 X_{i-5} + \beta_{\text{SMA}} \, \text{SMA}(X_i, 5) \] Then by taking \(\beta_0 = 0\), \(\beta_i = -1\) for \(1 \leq i \leq 5\) and \(\beta_{\text{SMA}} = 5\) we can immediately recover the variable \(X_i\). Though this may seem like a moot point, it was not taken into account in earlier versions of the project leading to the simplest model (linear regression) somehow dominating all other models.
simple_moving_average <- function(my_vec, lag_period) {
#' Takes the running average of a column vector
#'
#' Creates a new column vector whose entries are the average of the previous lag_period entries.
#' When not enough data is available to take the average over lag_period, the closest possible
#' average will be taken (for example, if lag_period = 10, then the first 2nd entry of the output
#' vector will simply be the average of the first two values, the 3rd entry of the output vector
#' will be the average of the first three values, and so forth.)
#'
#' @param my_vec the column vector to take the average values of
#' @param lag_period the number of days one wishes to average over
#'
#' @return A vector whose entries represent the average of the previous lag_period entries in my_vec
# Error handling
if(is.vector(my_vec) == FALSE){
stop("Not Vector: First argument of simple_moving_average must be a vector")
}
if(is.numeric(my_vec[1]) == FALSE){
stop("Non-numeric Entries: values of vector in first argument must be numeric.")
}
if(is.numeric(lag_period) == FALSE || lag_period != round(lag_period)){
stop("Not Integer: Second argument of simple_moving_average must be an integer larger than or equal to 2")
}
if(lag_period <= 1){
stop("Not Large Enough: Second argument of simple_moving_average must be an integer larger than or equal to 2")
}
# return variable
output_vec = c()
output_vec[1] = my_vec[1]
output_vec[2] = my_vec[2]
for (i in 3:length(my_vec)) {
# If there are less that lag_period of data previous to the current date,
# simply take the average of all the days prior to get the closest thing
# to a running average
if (i <= lag_period + 1){
output_vec[i] = mean(my_vec[1:(i-1)])
}
else {
output_vec[i] = mean(my_vec[(i-lag_period):(i-1)])
}
}
return(output_vec)
}
# Obtain four simple moving averages of AMD
AMD$SMA_cl_1W <- simple_moving_average(AMD$Close, 5)
AMD$SMA_cl_2W <- simple_moving_average(AMD$Close, 10)
AMD$SMA_cl_1M <- simple_moving_average(AMD$Close, 20)
AMD$SMA_cl_2M <- simple_moving_average(AMD$Close, 40)
# Obtain four simple moving averages of NVDA
NVDA$SMA_cl_1W <- simple_moving_average(NVDA$Close, 5)
NVDA$SMA_cl_2W <- simple_moving_average(NVDA$Close, 10)
NVDA$SMA_cl_1M <- simple_moving_average(NVDA$Close, 20)
NVDA$SMA_cl_2M <- simple_moving_average(NVDA$Close, 40)
# Obtain four simple moving averages of INTC
INTC$SMA_cl_1W <- simple_moving_average(INTC$Close, 5)
INTC$SMA_cl_2W <- simple_moving_average(INTC$Close, 10)
INTC$SMA_cl_1M <- simple_moving_average(INTC$Close, 20)
INTC$SMA_cl_2M <- simple_moving_average(INTC$Close, 40)
# Obtain four simple moving averages of TSM
TSM$SMA_cl_1W <- simple_moving_average(TSM$Close, 5)
TSM$SMA_cl_2W <- simple_moving_average(TSM$Close, 10)
TSM$SMA_cl_1M <- simple_moving_average(TSM$Close, 20)
TSM$SMA_cl_2M <- simple_moving_average(TSM$Close, 40)
# Plot the simple moving averages for each stock
g1<-ggplot(data = AMD, aes(x=Date)) +
geom_line(aes(y = SMA_cl_1W, color = '1-Week')) +
geom_line(aes(y = SMA_cl_2W, color = '2-Week')) +
geom_line(aes(y = SMA_cl_1M, color = '1-Month')) +
geom_line(aes(y = SMA_cl_2M, color = '2-Month')) +
ylab('USD') +
scale_color_manual(values = c(
'1-Week' = 'firebrick1',
'2-Week' = 'chocolate1',
'1-Month' = 'gold',
'2-Month' = 'chartreuse'
)) +
xlab('Date') +
ggtitle("(AMD) Simple Moving Average") +
scale_y_continuous( labels = label_comma()) +
theme_dark()
g2<-ggplot(data = NVDA, aes(x=Date)) +
geom_line(aes(y = SMA_cl_1W, color = '1-Week')) +
geom_line(aes(y = SMA_cl_2W, color = '2-Week')) +
geom_line(aes(y = SMA_cl_1M, color = '1-Month')) +
geom_line(aes(y = SMA_cl_2M, color = '2-Month')) +
ylab('USD') +
scale_color_manual(values = c(
'1-Week' = 'firebrick1',
'2-Week' = 'chocolate1',
'1-Month' = 'gold',
'2-Month' = 'chartreuse'
)) +
xlab('Date') +
ggtitle("(NVDA) Simple Moving Average") +
scale_y_continuous( labels = label_comma()) +
theme_dark()
g3<-ggplot(data = INTC, aes(x=Date)) +
geom_line(aes(y = SMA_cl_1W, color = '1-Week')) +
geom_line(aes(y = SMA_cl_2W, color = '2-Week')) +
geom_line(aes(y = SMA_cl_1M, color = '1-Month')) +
geom_line(aes(y = SMA_cl_2M, color = '2-Month')) +
ylab('USD') +
scale_color_manual(values = c(
'1-Week' = 'firebrick1',
'2-Week' = 'chocolate1',
'1-Month' = 'gold',
'2-Month' = 'chartreuse'
)) +
xlab('Date') +
ggtitle("(INTC) Simple Moving Average") +
scale_y_continuous( labels = label_comma()) +
theme_dark()
g4<-ggplot(data = TSM, aes(x=Date)) +
geom_line(aes(y = SMA_cl_1W, color = '1-Week')) +
geom_line(aes(y = SMA_cl_2W, color = '2-Week')) +
geom_line(aes(y = SMA_cl_1M, color = '1-Month')) +
geom_line(aes(y = SMA_cl_2M, color = '2-Month')) +
ylab('USD') +
scale_color_manual(values = c(
'1-Week' = 'firebrick1',
'2-Week' = 'chocolate1',
'1-Month' = 'gold',
'2-Month' = 'chartreuse'
)) +
xlab('Date') +
ggtitle("(TSM) Simple Moving Average") +
scale_y_continuous(labels = label_comma()) +
theme_dark()
wrapper <- swipeRwrapper(
tags$div(style = "width: 700px; margin: auto;", ggplotly(g1)),
tags$div(style = "width: 700px; margin: auto;", ggplotly(g2)),
tags$div(style = "width: 700px; margin: auto;", ggplotly(g3)),
tags$div(style = "width: 700px; margin: auto;", ggplotly(g4))
)
swipeR(wrapper, height = "400px", navigationColor = "navy")One characteristic that immediately becomes apparent is that evaluating the simple moving averages instead of the closing costs seems to “smooth out” the curves — in other words, the simple moving average average is much more stable and is not affected by a share’s volatility as much as our original predictors. Mathematically, this is simply a consequence of the fact that we are slowly interpolating the data with the overall average; since the overall average is a constant function (and thus infinitely differentiable), the “smoothing out” process really just us interpolating with a \(C^\infty(\mathbb{R})\) function.
Taking different values for the “lag period” of the simple moving average also has several benefits. Since the simple moving average measures the trends of a stock over that lag period, the simple moving average taken over 2 months (i.e. roughly 40 business days) measures long term behavior much more accurately than the simply moving average taken over 2 weeks (i.e. roughly 10 business days) , which would be a much better measure of short term trends. In fact, two SMA curves crossing tends to be a common trading signal for investors — when a short-period SMA curve crosses below a long-period SMA curve, this indicates the recent behavior of the stock is performing worse than its long term trends, meaning an investor would want to short the stock (i.e. invest based on the assumption the value of the stock will decrease). Similarly, when a short-period SMA curve crosses above a long-period SMA curve, this should indicate to investors that the stock is performing better than usual and thus it is a good idea to go long (i.e. purchase the stock ).
Exponential Moving Average
While the simple moving average (SMA) above can be incredibly useful to de-correlate the lag variables and measure the trends and behaviors of a given security, it can fall short in one particular way: it gives equal weight to events that happened at the beginning of the lag period as it does to events that happened at the end of the lag period. This is generally not the case with time series problems, as the relevancy of data points tends to decay non-linearly over time. A better model would be to weight recent events more heavily than events further in the past, and then take the average of these \(\text{values} \times \text{weights}\) — this is precisely the idea behind the exponential moving average. Specifically, we fix some smoothing factor \(\beta > 0\) define our weight to be \(w = \beta\, /\, (n+1)\) and the exponential moving average as \[ \operatorname{EMA}(X_i, n) = w \cdot X_{i}\ +\ (1-w) \cdot\operatorname{EMA}(X_{i-1}, n) \] where \(n\) is the number of days in our lag period. Its common in finance to take \(\beta = 2\), which is precisely the value we will take for the purpose of this project.
exponential_moving_average_helper <- function(my_vec, lag_period, smoothing_factor) {
#' Helper function to evaluate the exponential moving average over a fixed period
#' using an array buffer
#'
#' Creates a new column vector whose entries are the average of the previous lag_period entries.
#' When not enough data is available to take the average over lag_period, the closest possible
#' average will be taken (for example, if lag_period = 10, then the first 2nd entry of the output
#' vector will simply be the average of the first two values, the 3rd entry of the output vector
#' will be the average of the first three values, and so forth.)
#'
#' @param my_vec the column vector to take the average values of
#' @param lag_period the number of days one wishes to average over
#'
#' @return A vector whose entries represent the average of the previous lag_period entries in my_vec
# Error handling
if(is.vector(my_vec) == FALSE){
stop("Not Vector: First argument of simple_moving_average must be a vector")
}
if(is.numeric(my_vec[1]) == FALSE){
stop("Non-numeric Entries: values of vector in first argument must be numeric.")
}
if(is.numeric(lag_period) == FALSE || lag_period != round(lag_period)){
stop("Not Integer: Second argument of simple_moving_average must be an integer larger than or equal to 2")
}
if(lag_period <= 1){
stop("Not Large Enough: Second argument of simple_moving_average must be an integer larger than or equal to 2")
}
vec_len <- length(my_vec)
output_vec <- c()
output_vec[1] <- my_vec[1]
for (i in 2:vec_len) {
output_vec[i] <- smoothing_factor * my_vec[i] + (1 - smoothing_factor) * output_vec[i-1]
}
return(output_vec[vec_len])
}
exponential_moving_average <- function(my_vec, lag_period) {
#' Takes the running average of a column vector
#'
#' Creates a new column vector whose entries are the average of the previous lag_period entries.
#' When not enough data is available to take the average over lag_period, the closest possible
#' average will be taken (for example, if lag_period = 10, then the first 2nd entry of the output
#' vector will simply be the average of the first two values, the 3rd entry of the output vector
#' will be the average of the first three values, and so forth.)
#'
#' @param my_vec the column vector to take the average values of
#' @param lag_period the number of days one wishes to average over
#'
#' @return A vector whose entries represent the average of the previous lag_period entries in my_vec
# Error handling
if(is.vector(my_vec) == FALSE){
stop("Not Vector: First argument of simple_moving_average must be a vector")
}
if(is.numeric(my_vec[1]) == FALSE){
stop("Non-numeric Entries: values of vector in first argument must be numeric.")
}
if(is.numeric(lag_period) == FALSE || lag_period != round(lag_period)){
stop("Not Integer: Second argument of simple_moving_average must be an integer larger than or equal to 2")
}
if(lag_period <= 1){
stop("Not Large Enough: Second argument of simple_moving_average must be an integer larger than or equal to 2")
}
output_vec <- c()
output_vec[1] <- my_vec[1]
output_vec[2] <- my_vec[1]
smoothing_factor = 2/(lag_period+1)
for (i in 3:length(my_vec)) {
# If there are less that lag_period of data previous to the current date,
# simply take the average of all the days prior to get the closest thing
# to a running average
if (i <= (lag_period+1)){
output_vec[i] = exponential_moving_average_helper(my_vec[1:(i-1)], i, 2/(i + 1))
}
else {
output_vec[i] = exponential_moving_average_helper(my_vec[(i-lag_period ):(i-1)], lag_period, smoothing_factor)
}
}
return(output_vec)
}
# Compute Exponential moving average for AMD
AMD$EMA_cl_1W <- exponential_moving_average(AMD$Close, 5)
AMD$EMA_cl_2W <- exponential_moving_average(AMD$Close, 10)
AMD$EMA_cl_1M <- exponential_moving_average(AMD$Close, 20)
AMD$EMA_cl_2M <- exponential_moving_average(AMD$Close, 40)
# Compute Exponential moving average for NVDA
NVDA$EMA_cl_1W <- exponential_moving_average(NVDA$Close, 5)
NVDA$EMA_cl_2W <- exponential_moving_average(NVDA$Close, 10)
NVDA$EMA_cl_1M <- exponential_moving_average(NVDA$Close, 20)
NVDA$EMA_cl_2M <- exponential_moving_average(NVDA$Close, 40)
# Compute Exponential moving average for INTC
INTC$EMA_cl_1W <- exponential_moving_average(INTC$Close, 5)
INTC$EMA_cl_2W <- exponential_moving_average(INTC$Close, 10)
INTC$EMA_cl_1M <- exponential_moving_average(INTC$Close, 20)
INTC$EMA_cl_2M <- exponential_moving_average(INTC$Close, 40)
# Compute Exponential moving average for TSM
TSM$EMA_cl_1W <- exponential_moving_average(TSM$Close, 5)
TSM$EMA_cl_2W <- exponential_moving_average(TSM$Close, 10)
TSM$EMA_cl_1M <- exponential_moving_average(TSM$Close, 20)
TSM$EMA_cl_2M <- exponential_moving_average(TSM$Close, 40)
# Plot the exponential moving average for each stock
g1<-ggplot(data = AMD, aes(x=Date)) +
geom_line(aes(y = EMA_cl_1W, color = '1-Week')) +
geom_line(aes(y = EMA_cl_2W, color = '2-Week')) +
geom_line(aes(y = EMA_cl_1M, color = '1-Month')) +
geom_line(aes(y = EMA_cl_2M, color = '2-Month')) +
ylab('USD') +
scale_color_manual(values = c(
'1-Week' = 'firebrick1',
'2-Week' = 'chocolate1',
'1-Month' = 'gold',
'2-Month' = 'chartreuse'
)) +
xlab('Date') +
ggtitle("(AMD) Exponential Moving Average") +
scale_y_continuous( labels = label_comma()) +
theme_dark()
g2<-ggplot(data = NVDA, aes(x=Date)) +
geom_line(aes(y = EMA_cl_1W, color = '1-Week')) +
geom_line(aes(y = EMA_cl_2W, color = '2-Week')) +
geom_line(aes(y = EMA_cl_1M, color = '1-Month')) +
geom_line(aes(y = EMA_cl_2M, color = '2-Month')) +
ylab('USD') +
scale_color_manual(values = c(
'1-Week' = 'firebrick1',
'2-Week' = 'chocolate1',
'1-Month' = 'gold',
'2-Month' = 'chartreuse'
)) +
xlab('Date') +
ggtitle("(NVDA) Exponential Moving Average") +
scale_y_continuous( labels = label_comma()) +
theme_dark()
g3<-ggplot(data = INTC, aes(x=Date)) +
geom_line(aes(y = EMA_cl_1W, color = '1-Week')) +
geom_line(aes(y = EMA_cl_2W, color = '2-Week')) +
geom_line(aes(y = EMA_cl_1M, color = '1-Month')) +
geom_line(aes(y = EMA_cl_2M, color = '2-Month')) +
ylab('USD') +
scale_color_manual(values = c(
'1-Week' = 'firebrick1',
'2-Week' = 'chocolate1',
'1-Month' = 'gold',
'2-Month' = 'chartreuse'
)) +
xlab('Date') +
ggtitle("(INTC) Exponential Moving Average") +
scale_y_continuous( labels = label_comma()) +
theme_dark()
g4<-ggplot(data = TSM, aes(x=Date)) +
geom_line(aes(y = EMA_cl_1W, color = '1-Week')) +
geom_line(aes(y = EMA_cl_2W, color = '2-Week')) +
geom_line(aes(y = EMA_cl_1M, color = '1-Month')) +
geom_line(aes(y = EMA_cl_2M, color = '2-Month')) +
ylab('USD') +
scale_color_manual(values = c(
'1-Week' = 'firebrick1',
'2-Week' = 'chocolate1',
'1-Month' = 'gold',
'2-Month' = 'chartreuse'
)) +
xlab('Date') +
ggtitle("(TSM) Exponential Moving Average") +
scale_y_continuous( labels = label_comma()) +
theme_dark()
wrapper <- swipeRwrapper(
tags$div(style = "width: 700px; margin: auto;", ggplotly(g1)),
tags$div(style = "width: 700px; margin: auto;", ggplotly(g2)),
tags$div(style = "width: 700px; margin: auto;", ggplotly(g3)),
tags$div(style = "width: 700px; margin: auto;", ggplotly(g4))
)
swipeR(wrapper, height = "400px", navigationColor = "navy")While the plots of the simple moving average and exponential average look very similar to one another, one notable difference is that the exponential average does not “smooth out” the curve as much — this is simply due to the fact that any recent volatility affects the exponential moving average much more than the simple moving average, since volatility in recent events is weighed more heavily.
Simple Moving Deviation of Closing Price
With a concrete notion of the simple moving average as the mean taken
over our lag variables, a natural extension is to also measure the
standard deviation of our lag variables as well to gain an accurate
insight on the volatility of our time series. In fact, the simple moving
deviation has a very particular name in terms of stock prices:
Bollinger Bands. In the very first graphs plotted at
the beginning of this analysis (in the Loading Packages and
Data chapter) using the chartSeries command, the
gray-dotted lines running through the stock values is simply the 20-day
SMA, and the red-dotted lines above and below the stock values are the
20-day simple moving deviation (which are referred to in the chart as
the Bollinger bands). For the sake of completeness, the simple moving
deviation can be calculated as \[
\operatorname{SMD}(X_i, n) = \sqrt{ \frac{\sum_{j=0}^{n-1} (X_{i-j} -
\operatorname{SMA}(X_i, n))^2}{n-1} }
\] As with the simple moving average, we could alternatively
consider the variation where the current data point is not accounted for
in the simple moving deviation by instead ranging \(1 \leq j \leq n\).
simple_moving_deviation <- function(my_vec, lag_period) {
#' Takes the running standard deviation of a column vector
#'
#' Creates a new column vector whose entries are the standard deviation of the previous lag_period entries.
#' When not enough data is available to take the deviation over lag_period, the closest possible
#' average will be taken (for example, if lag_period = 10, then the first 2nd entry of the output
#' vector will simply be the average of the first two values, the 3rd entry of the output vector
#' will be the average of the first three values, and so forth.)
#'
#' @param my_vec the column vector to take the standard deviation of
#' @param lag_period the number of days one wishes to average over
#'
#' @return A vector whose entries represent the standard deviation of the previous lag_period entries in my_vec
# Error handling
if(is.vector(my_vec) == FALSE){
stop("Not Vector: First argument of simple_moving_average must be a vector")
}
if(is.numeric(my_vec[1]) == FALSE){
stop("Non-numeric Entries: values of vector in first argument must be numeric.")
}
if(is.numeric(lag_period) == FALSE || lag_period != round(lag_period)){
stop("Not Integer: Second argument of simple_moving_average must be an integer larger than or equal to 2")
}
if(lag_period <= 1){
stop("Not Large Enough: Second argument of simple_moving_average must be an integer larger than or equal to 2")
}
# return variable
output_vec = c()
# Setting the first standard deviation to 0 and beginning the loop
# at 2 prevents a divide by 0 error without adding an additional if-else branch
# in the loop
output_vec[1] = 0
output_vec[2] = 0
for (i in 3:length(my_vec)) {
# If there are less that lag_period of data previous to the current date,
# simply take the average of all the days prior to get the closest thing
# to a running average
if (i <= lag_period+1){
output_vec[i] = sd(my_vec[1:(i-1)])
}
else {
output_vec[i] = sd(my_vec[(i-lag_period):(i-1)])
}
}
return(output_vec)
}
# Compute the simple moving deviation of AMD
AMD$SMD_cl_1W <- simple_moving_deviation(AMD$Close, 5)
AMD$SMD_cl_2W <- simple_moving_deviation(AMD$Close, 10)
AMD$SMD_cl_1M <- simple_moving_deviation(AMD$Close, 20)
AMD$SMD_cl_2M <- simple_moving_deviation(AMD$Close, 40)
# Compute the simple moving deviation of NVDA
NVDA$SMD_cl_1W <- simple_moving_deviation(NVDA$Close, 5)
NVDA$SMD_cl_2W <- simple_moving_deviation(NVDA$Close, 10)
NVDA$SMD_cl_1M <- simple_moving_deviation(NVDA$Close, 20)
NVDA$SMD_cl_2M <- simple_moving_deviation(NVDA$Close, 40)
# Compute the simple moving deviation of INTC
INTC$SMD_cl_1W <- simple_moving_deviation(INTC$Close, 5)
INTC$SMD_cl_2W <- simple_moving_deviation(INTC$Close, 10)
INTC$SMD_cl_1M <- simple_moving_deviation(INTC$Close, 20)
INTC$SMD_cl_2M <- simple_moving_deviation(INTC$Close, 40)
# Compute the simple moving deviation of TSM
TSM$SMD_cl_1W <- simple_moving_deviation(TSM$Close, 5)
TSM$SMD_cl_2W <- simple_moving_deviation(TSM$Close, 10)
TSM$SMD_cl_1M <- simple_moving_deviation(TSM$Close, 20)
TSM$SMD_cl_2M <- simple_moving_deviation(TSM$Close, 40)
# Plot the simple moving deviation for our four stocks
g1<-ggplot(data = AMD, aes(x=Date)) +
geom_line(aes(y = SMD_cl_1W, color = '1-Week')) +
geom_line(aes(y = SMD_cl_2W, color = '2-Week')) +
geom_line(aes(y = SMD_cl_1M, color = '1-Month')) +
geom_line(aes(y = SMD_cl_2M, color = '2-Month')) +
ylab('USD') +
scale_color_manual(values = c(
'1-Week' = 'firebrick1',
'2-Week' = 'chocolate1',
'1-Month' = 'gold',
'2-Month' = 'chartreuse'
)) +
xlab('Date') +
ggtitle("(AMD) Simple Moving Deviation") +
scale_y_continuous( labels = label_comma()) +
theme_dark()
g2<-ggplot(data = NVDA, aes(x=Date)) +
geom_line(aes(y = SMD_cl_1W, color = '1-Week')) +
geom_line(aes(y = SMD_cl_2W, color = '2-Week')) +
geom_line(aes(y = SMD_cl_1M, color = '1-Month')) +
geom_line(aes(y = SMD_cl_2M, color = '2-Month')) +
ylab('USD') +
scale_color_manual(values = c(
'1-Week' = 'firebrick1',
'2-Week' = 'chocolate1',
'1-Month' = 'gold',
'2-Month' = 'chartreuse'
)) +
xlab('Date') +
ggtitle("(NVDA) Simple Moving Deviation") +
scale_y_continuous( labels = label_comma()) +
theme_dark()
g3<-ggplot(data = INTC, aes(x=Date)) +
geom_line(aes(y = SMD_cl_1W, color = '1-Week')) +
geom_line(aes(y = SMD_cl_2W, color = '2-Week')) +
geom_line(aes(y = SMD_cl_1M, color = '1-Month')) +
geom_line(aes(y = SMD_cl_2M, color = '2-Month')) +
ylab('USD') +
scale_color_manual(values = c(
'1-Week' = 'firebrick1',
'2-Week' = 'chocolate1',
'1-Month' = 'gold',
'2-Month' = 'chartreuse'
)) +
xlab('Date') +
ggtitle("(INTC) Simple Moving Deviation") +
scale_y_continuous( labels = label_comma()) +
theme_dark()
g4<-ggplot(data = TSM, aes(x=Date)) +
geom_line(aes(y = SMD_cl_1W, color = '1-Week')) +
geom_line(aes(y = SMD_cl_2W, color = '2-Week')) +
geom_line(aes(y = SMD_cl_1M, color = '1-Month')) +
geom_line(aes(y = SMD_cl_2M, color = '2-Month')) +
ylab('USD') +
scale_color_manual(values = c(
'1-Week' = 'firebrick1',
'2-Week' = 'chocolate1',
'1-Month' = 'gold',
'2-Month' = 'chartreuse'
)) +
xlab('Date') +
ggtitle("(TSM) Simple Moving Deviation") +
scale_y_continuous( labels = label_comma()) +
theme_dark()
wrapper <- swipeRwrapper(
tags$div(style = "width: 700px; margin: auto;", ggplotly(g1)),
tags$div(style = "width: 700px; margin: auto;", ggplotly(g2)),
tags$div(style = "width: 700px; margin: auto;", ggplotly(g3)),
tags$div(style = "width: 700px; margin: auto;", ggplotly(g4))
)
swipeR(wrapper, height = "400px", navigationColor = "navy")Ultimately, plotting our simple moving deviation without the relevant simple moving averages is not terribly insightful; as one would expect, there is more variation across longer periods than there is across shorter periods.
Moving Average Convergence Divergence (MACD)
The last metric we wish to consider in this analysis is closely related to the crossing of SMA curves in the subsection above; the Moving Average Convergence Divergence (MACD) is simply a value which indicates the curve crossing of the exponential moving average, as that carries greater information about the recent behavior of the data. In most stock market analysis, it is standard to consider the short-term EMA to be taken over a 2-week period and the long-term EMA to be taken over a 1-month period; thus, we define \[ \operatorname{MACD}(X_i) = \operatorname{EMA}(X_i,\ 10) - \operatorname{EMA}(X_i,\ 20) \] In the general study of time-series, the MACD is just a particular example of a curve-crossing metric between moving averages used to predict trend changes — any two long-term and short-term periods could be used to define a similar metric. In the graphs below, a curve crossing the horizontal axis acts as a trade signal to investors to either purchase or short the security depending on whether the curve is crossing into the upper or lower half-plane.
# Compute the MACD for our 4 stocks
AMD$MACD = (AMD$EMA_cl_2W - AMD$EMA_cl_1M)
NVDA$MACD = (NVDA$EMA_cl_2W - NVDA$EMA_cl_1M)
INTC$MACD = (INTC$EMA_cl_2W - INTC$EMA_cl_1M)
TSM$MACD = (TSM$EMA_cl_2W - TSM$EMA_cl_1M)
# Plot the MACD line
g1<-ggplot(data = AMD, aes(x=Date)) +
geom_line(aes(y = MACD, color = 'MACD')) +
ylab('USD') +
scale_color_manual(values = c(
'MACD' = 'aquamarine'
)) +
xlab('Date') +
ggtitle("AMD MACD Line") +
scale_y_continuous( labels = label_comma()) +
theme_dark()
g2<-ggplot(data = NVDA, aes(x=Date)) +
geom_line(aes(y = MACD, color = 'MACD')) +
ylab('USD') +
scale_color_manual(values = c(
'MACD' = 'aquamarine'
)) +
xlab('Date') +
ggtitle("NVDA MACD Line") +
scale_y_continuous( labels = label_comma()) +
theme_dark()
g3<-ggplot(data = INTC, aes(x=Date)) +
geom_line(aes(y = MACD, color = 'MACD')) +
ylab('USD') +
scale_color_manual(values = c(
'MACD' = 'aquamarine'
)) +
xlab('Date') +
ggtitle("INTC MACD Line") +
scale_y_continuous( labels = label_comma()) +
theme_dark()
g4<-ggplot(data = TSM, aes(x=Date)) +
geom_line(aes(y = MACD, color = 'MACD')) +
ylab('USD') +
scale_color_manual(values = c(
'MACD' = 'aquamarine'
)) +
xlab('Date') +
ggtitle("TSM MACD Line") +
scale_y_continuous( labels = label_comma()) +
theme_dark()
wrapper <- swipeRwrapper(
tags$div(style = "width: 700px; margin: auto;", ggplotly(g1)),
tags$div(style = "width: 700px; margin: auto;", ggplotly(g2)),
tags$div(style = "width: 700px; margin: auto;", ggplotly(g3)),
tags$div(style = "width: 700px; margin: auto;", ggplotly(g4))
)
swipeR(wrapper, height = "400px", navigationColor = "navy")Data Correlation
Before setting up our statistical learning models, it is crucial to understand whether there are any underlying relationships between the initial predictors or the added predictors we have defined in the previous section. Based on our prior discussion, it is natural to expect that our lag variables are all heavily correlated with each other, only slightly decreasing the longer the lag period; in fact, the same should be true of the moving averages as the lag period increases. Since each of our four stocks have the same predictors, we only provide a complete correlation plot for one of the stocks (AMD) to confirm the above hypothesis that the added predictors (aside fom MACD) are heavily correlated with the closing price; for the other three stocks, we simply examine the remaining predictors.
select(TSM, is.numeric) %>%
cor() %>%
corrplot(method = "circle", type = "lower", diag = FALSE, tl.cex=0.6, number.cex=0.3, title="Correlation Plot")
select(NVDA, Close, Volume, SMD_cl_1W, SMD_cl_2W, SMD_cl_1M, SMD_cl_2M, MACD) %>%
cor() %>%
corrplot(method = "circle", type = "lower", diag = FALSE, tl.cex=0.6, title="NVDA Correlation Plot" )
select(INTC, Close, Volume, SMD_cl_1W, SMD_cl_2W, SMD_cl_1M, SMD_cl_2M, MACD) %>%
cor() %>%
corrplot(method = "circle", type = "lower", diag = FALSE, tl.cex=0.6, title="INTC Correlation Plot")
select(TSM, Close, Volume, SMD_cl_1W, SMD_cl_2W, SMD_cl_1M, SMD_cl_2M, MACD) %>%
cor() %>%
corrplot(method = "circle", type = "lower", diag = FALSE, tl.cex=0.6, title="TSM Correlation Plot")
From the above plots we are able to confirm that there is in fact a heavy correlation between the closing price and each of the lag variables which decreases with the lag period (as expected). In addition, we see that all of our initial variables aside from volume (i.e. Low, High, Open, Close, and Adjusted) are all highly correlated with one another; to an extend, this tells us that all of the variables follow the same underlying market trend so we could hypothetically use any one to predict the other. Lastly, we see a somewhat unexpected correlation between the 1-week standard moving deviation of the closing price and the volume of securities sold on a given day for each of the four stocks.
Setting Up Models
With a good picture in mind of how our stock prices can be measured from both the Yahoo Finance and added metrics, we can resample our data to better ensure that our models are accurately describing the relationships between our predictors an outcome instead of the properties of the data points. This will be done in several steps, first preparing the data to ensure that our models do not become over-fitted to a particular subset. After the data is sufficiently organized into well-balanced partitions, we begin applying a variety of models to the training data to see how the model’s predictions line up with the actual outcomes.
Data Split
One of the primary ways we ensure robustness of any model is by partitioning our data into training and testing data. Foremost, this ensures that our model does not become over-fit to the details and noise of our underlying data-set by introducing a portion of the data which is unseen during the training phase (i.e. the testing data). Ultimately, one would want outcome variable to have similar statistics / variance across both the training and testing sets — this is accomplished by stratifying our split about the desired outcome variable.
# Create 70-30 split for AMD data
AMD_split <- initial_split(AMD, prop = 0.7, strata = Close)
AMD_train <- training(AMD_split)
AMD_test <- testing(AMD_split)
# Create 70-30 split for NVDA data
NVDA_split <- initial_split(NVDA, prop = 0.7, strata = Close)
NVDA_train <- training(NVDA_split)
NVDA_test <- testing(NVDA_split)
# Create 70-30 split for INTC data
INTC_split <- initial_split(INTC, prop = 0.7, strata = Close)
INTC_train <- training(INTC_split)
INTC_test <- testing(INTC_split)
# Create 70-30 split for TSM data
TSM_split <- initial_split(TSM, prop = 0.7, strata = Close)
TSM_train <- training(TSM_split)
TSM_test <- testing(TSM_split)Recipe Creation
One of the most important steps in setting up a statistical learning model is telling the model which predictors to include and how to preprocess the data in our training set. Despite the fact that we have a wide range of predictors at our disposal, adding more predictors does not always indicate better performance of our model — for example, we have defined multiple versions of the simple moving average metric for each stock, though they may not all be simultaneously useful. Therefore we wish to judiciously choose the predictors which we will base each of our models off of.
Despite the high correlation between our lag variables, auto-regression tends to provide successful rudimentary models for predicting market behavior — for example, ARIMA (Autoregressive Integrated Moving Average, also known as Box-Jenkins) is one of the most popular forecasting methods for time-series problems. Consequently, we want to ensure that all of our lag variables are present for the regression algorithms. In addition, we also want to incorporate any common trading signals among investors: for example, since the strategy of investors changes with the sign of the MACD value, this lead to propagated trends which we would like our model to pick up on. Finally, we add all short-term (1 week) metrics of data — however, it should be mentioned that the addition of these variables are the least motivated (for example, EMA is already used in MACD and SMA serves a very similar purpose).
Using the tidymodels package, we are able to specify
which variables are used to predict the outcome and process our data via
the recipe data structure — though it is effectively the
same for each individual stock, we need to repeat this step since the
variable names pulled from loadSymbols do differ.
# Create AMD recipe
AMD_recipe = recipe(Close ~ Close_L1 + Close_L2 + Close_L3 + Close_L4 + Close_L5 +
Close_L6 + Close_L7 + Close_L8 + Close_L9 + Close_L10 +
SMD_cl_1W + EMA_cl_1W + SMA_cl_1W + MACD,
data=AMD_train) %>%
step_center(all_predictors()) %>%
step_scale(all_predictors())
# Create NVDA recipe
NVDA_recipe = recipe(Close ~ Close_L1 + Close_L2 + Close_L3 + Close_L4 + Close_L5 +
Close_L6 + Close_L7 + Close_L8 + Close_L9 + Close_L10 +
SMD_cl_1W + SMA_cl_1W + EMA_cl_1W + MACD,
data=NVDA_train) %>%
step_center(all_predictors()) %>%
step_scale(all_predictors())
# Create INTC recipe
INTC_recipe = recipe(Close ~ Close_L1 + Close_L2 + Close_L3 + Close_L4 +
Close_L5 + Close_L6 + Close_L7 + Close_L8 + Close_L9 + Close_L10 +
SMD_cl_1W + SMA_cl_1W + EMA_cl_1W + MACD,
data=INTC_train) %>%
step_center(all_predictors()) %>%
step_scale(all_predictors())
# Create TSM recipe
TSM_recipe = recipe(Close ~ Close_L1 + Close_L2 + Close_L3 + Close_L4 +
Close_L5 + Close_L6 + Close_L7 + Close_L8 + Close_L9 + Close_L10 +
SMD_cl_1W + SMA_cl_1W + EMA_cl_1W + MACD,
data=TSM_train) %>%
step_center(all_predictors()) %>%
step_scale(all_predictors())k-Fold Cross Validation
As each of our datasets contain a relatively small number of observations (N = 250) taken across 2023, it is important to maximize the utility we are getting out of our data. The purpose of k-fold cross validation is to ensure that our models do not become overfit to any particular subset of observations by rotating exactly \(k \geq 1\) subsets of our data between training data and validation. Specifically, we split our data into \(k=10\) (roughly) equal sized subsets, stratified about our outcome variable to ensure that there is (roughly) equal variance in each subset. Using these 10 subsets, we train each model 10 times, where precisely one subset is taken to be the validation data while the remaining 9 subsets serve as training data.
AMD_folds <- vfold_cv(AMD_train, v = 10, strata = Close)
NVDA_folds <- vfold_cv(NVDA_train, v = 10, strata = Close)
INTC_folds <- vfold_cv(INTC_train, v = 10, strata = Close)
TSM_folds <- vfold_cv(TSM_train, v = 10, strata = Close)Define Workflows
Despite the fact that our analysis is on how statistical learning models can predict semiconductor stocks, we have not actually described any particular statistical learning algorithms up to this point — this is where the modelling step comes in. Each of our models is equivalently its own statistical learning algorithm, with added hyperparameter tuning to distinguish certain algorithms from others (for example, ridge and lasso regression). For the scope of this project, we will explore the application of seven machine learning models:
Linear Regression
Our first and most basic model is that of standard linear regression; if we assume that our data follows a linear trend in terms of the predictors, the goal of linear regression is to find the best coefficients \(\beta_i\) such that our outcome variable \(Y\) can be described by \[ Y = \beta_0 + \sum_{i=1}^p \beta_i \vec{X_i} \] This is typically done by minimizing the residual sum of squares \[ \operatorname{RSS}=\sum_{i=1}^n (y_i - \sum_{j=1}^p \beta_j x_{ij})^2 \] on our sample set. Though somewhat rudimentary compared to some of our other models, statistical learning through linear regression lends itself quite well to mechanistic models whenever there is some sort of linear relationship involved. In fact, (linear) auto-regression is one of the most popular models to fine-tune for time series problems such as stock market predictions. However, linear regression does not always perform as well as some of our other algorithms for empirically-driven problems due to its strong linearity assumption.
############################
# Define Model for Linear Regression
############################
linear_model <- linear_reg() %>%
set_engine("lm")Ridge Regression
A modified version of linear regression, ridge regression also attempts to fit sample data in a linear fashion subject to some additional constraints on the cost function. Instead of considering the usual residual sum of squares, a hyper-parameter \(\lambda\) which penalizes higher slopes in our formula: \[ \operatorname{RSS}_{\text{ridge}} = \sum_{i=1}^n (y_i - \sum_{j=1}^p \beta_j x_{ij})^2 + \lambda \sum_{i=1}^p \beta_i^2 \] It turns out that \(\lambda\) is precisely the Lagrange multiplier of the usual OLS minimization problem, now subject to the constraint \(\sum_{i=1}^p \beta^2 < c\) for some \(c > 0\) — geometrically this can be visualized as the intersection of our OLS level sets with the \(p\)-dimensional sphere of radius \(\sqrt{c}\):
Ultimately this makes the coefficients in our ridge model harder to interpret, since they are biased towards zero; however, this also prevents overfitting, by preventing the training data from making any of our coefficients too large.
############################
# Define Model for Ridge Regression
############################
ridge_model <- linear_reg(mixture = 0,
penalty = tune()) %>%
set_mode("regression") %>%
set_engine("glmnet")Lasso Regression
Also known as \(L^1\) regression, Lasso regression is very similar to ridge regression in the sense that it modifies the usual cost function in linear regression with a penalty term. The difference (as the name \(L^1\) regression suggests) is that the penalty term is now taken over the Hilbert space \(L^1(\mathbb{R}, \mu)\), as opposed to \(L^2(\mathbb{R}, \mu)\) for ridge regression. \[ \operatorname{RSS}_{\text{Lasso}} = \sum_{i=1}^n (y_i - \sum_{j=1}^p \beta_j x_{ij})^2 + \lambda \sum_{i=1}^p |\beta_i| \]
For those familiar with the theory of metric spaces, optimizing \(\operatorname{RSS}_{\text{Lasso}}\) can be geometrically interpreted as finding where the usual OLS level sets intersect the unit circle with respect to the taxicab metric.
############################
# Define Model for Lasso Regression
############################
lasso_model <- linear_reg(mixture = 1,
penalty = tune()) %>%
set_mode("regression") %>%
set_engine("glmnet")Elastic Net Regularization
Elastic net regularization is effectively the linear interpolation of ridge regression and lasso regression. By introducing a new parameter \(\alpha \in [0, 1]\), we may combine the above two penalty terms via \[ \operatorname{RSS}_{\text{EN}} = \sum_{i=1}^n (y_i - \sum_{j=1}^p \beta_j x_{ij})^2 + \lambda \big( \alpha \sum_{i=1}^p |\beta_i| + (1-\alpha) \sum_{i=1}^p \beta_i^2 \big) \] This ultimately allows us to combine the strengths of both lasso and ridge regression over regular regression, such as handling both high dimensional data and significant collinearity. In addition, with the right grid of hype-parameter values, we can generally expect that elastic net will perform just as well as lasso and ridge (if not better).
############################
# Define Model for Elastic Net
############################
elastic_net_model <- linear_reg(mixture = tune(),
penalty = tune()) %>%
set_mode("regression") %>%
set_engine("glmnet")k-Nearest Neighbors
Ultimately all of our regression models above assume some sort of functional (i.e. linear) form of a relationship between our predictors and outcome — that is, they are all parametric models. \(k\)-Nearest neighbors is our first example of a non-parametric model, in the sense that it does not make any prior assumptions about the relationship between our outcome and data. This ultimately makes \(k\)-nearest neighbors a much more viable option for predicting empirically-driven processes as well as classification problems; however, it should be noted that \(k\)-nearest neighbors is not commonly used with time-series problems (as there is often a very direct relationship with a predictor and its lag variables).
The idea behind \(k\)-nearest neighbors, however, is exactly what the name suggests: assuming we are able to establish some sort of metric space structure on our underlying data-set, we predict the outcome of a new data point based on the \(k\) nearest labelled values. For classification problems, this is done by assigning the class with the highest number of occurrences among the \(k\) nearest neighbors; for regression, several techniques such as weighted averages can be used.
One immediate observation that can be drawn from the
above picture is that the performance of \(k\)-nearest neighbors seems to depend on
the number \(k\) of neighbors taken
into consideration. We will see in the next sections that this is
absolutely the case, and we will ultimately need to vary \(k\) as a hyperparameter by testing it
against an array of models with varying values of \(k \geq 1\).
############################
# Define Model for k-Nearest Neighbors
############################
knn_model <- nearest_neighbor(neighbors = tune()) %>%
set_engine("kknn") %>%
set_mode("regression")Random Forest
Similar to \(k\)-nearest neighbors, forest-based algorithms are highliy non-linear and non-parametric models; however, they do not necessarily rely on the norm or metric of the underlying parameter space (which \(k\)-Nearest neighbors to determine what constitutes “nearest”). Instead, tree-based algorithms rely much more on the underlying topology of the parameter space \(X\), as one considers finite sub-covers of \(X\) where each open set contains at least one sample point. Random collections of observations in the training data are then sampled (in a process known as bootstrapping), and corresponding splits of the parameter space are taken. A decision tree is then assigned to describe the partition of the parameter space; any graph made up of a disjoint union of trees is known as a forest (hence the name random forest). While decision trees are individually highly sensitive to perturbations in the training data (e.g if the proportion of data above / below the average of a certain variable flips, nodes corresponding to that variable in the decision trees will generally swap as well), by taking the median or mode across the outcome of \(N \geq 1\) trees, we stabilize our model significantly.
############################
# Define Model for Random Forest
############################
random_forest_model <- rand_forest(mtry = tune(),
trees = tune(),
min_n = tune()) %>%
set_engine("ranger", importance = "impurity") %>%
set_mode("regression")Boosted Trees
Similar to random forests, an array of decision trees are created to each individually contribute to the outcome of a decision. However, boosted trees rely on the concept of gradient boosting (with respect to a loss function) in optimization / measure theory. In this case, tree are iteratively constructed in order to minimize the training error of the previous tree (with respect to some loss function). A learning rate is then established to dictation how much each decision tree weighs in on the outcome of the previous decision tree, thus leading to a highly flexible (but difficult to tune) model.
############################
# Define Model for Boosted Trees
############################
boosted_trees_model <- boost_tree(mtry = tune(),
trees = tune(),
learn_rate = tune()) %>%
set_engine("xgboost") %>%
set_mode("regression")With all seven of our statistical learning algorithms fixed (along
with tune() applied to relevant hyperparameters), we are
now able to load the models and recipes into workflows. Workflows are
objects introduced by the tidymodels package that allows us
to keep track of all pre-processing and modelling simultaneously, and
will be particularly useful in minimizing the number of objects to keep
track of since we will be dealing with seven models for each of our four
stocks.
############################
# Set Up Workflows for Linear Regression
############################
linear_wflow_AMD <- workflow() %>%
add_model(linear_model) %>%
add_recipe(AMD_recipe)
linear_wflow_NVDA <- workflow() %>%
add_model(linear_model) %>%
add_recipe(NVDA_recipe)
linear_wflow_INTC <- workflow() %>%
add_model(linear_model) %>%
add_recipe(INTC_recipe)
linear_wflow_TSM <- workflow() %>%
add_model(linear_model) %>%
add_recipe(TSM_recipe)
############################
# Set Up Workflows for Ridge Regression
############################
ridge_wflow_AMD <- workflow() %>%
add_model(ridge_model) %>%
add_recipe(AMD_recipe)
ridge_wflow_NVDA <- workflow() %>%
add_model(ridge_model) %>%
add_recipe(NVDA_recipe)
ridge_wflow_INTC <- workflow() %>%
add_model(ridge_model) %>%
add_recipe(INTC_recipe)
ridge_wflow_TSM <- workflow() %>%
add_model(ridge_model) %>%
add_recipe(TSM_recipe)
############################
# Set Up Workflows for Lasso Regression
############################
lasso_wflow_AMD <- workflow() %>%
add_model(lasso_model) %>%
add_recipe(AMD_recipe)
lasso_wflow_NVDA <- workflow() %>%
add_model(lasso_model) %>%
add_recipe(NVDA_recipe)
lasso_wflow_INTC <- workflow() %>%
add_model(lasso_model) %>%
add_recipe(INTC_recipe)
lasso_wflow_TSM <- workflow() %>%
add_model(lasso_model) %>%
add_recipe(TSM_recipe)
############################
# Set Up Workflows for Elastic Net
############################
elastic_net_wflow_AMD <- workflow() %>%
add_model(elastic_net_model) %>%
add_recipe(AMD_recipe)
elastic_net_wflow_NVDA <- workflow() %>%
add_model(elastic_net_model) %>%
add_recipe(NVDA_recipe)
elastic_net_wflow_INTC <- workflow() %>%
add_model(elastic_net_model) %>%
add_recipe(INTC_recipe)
elastic_net_wflow_TSM <- workflow() %>%
add_model(elastic_net_model) %>%
add_recipe(TSM_recipe)
############################
# Set Up Workflows for k-Nearest Neighbors
############################
knn_wflow_AMD <- workflow() %>%
add_model(knn_model) %>%
add_recipe(AMD_recipe)
knn_wflow_NVDA <- workflow() %>%
add_model(knn_model) %>%
add_recipe(NVDA_recipe)
knn_wflow_INTC <- workflow() %>%
add_model(knn_model) %>%
add_recipe(INTC_recipe)
knn_wflow_TSM <- workflow() %>%
add_model(knn_model) %>%
add_recipe(TSM_recipe)
############################
# Set Up Workflows for Random Forest
############################
random_forest_wflow_AMD <- workflow() %>%
add_model(random_forest_model) %>%
add_recipe(AMD_recipe)
random_forest_wflow_NVDA <- workflow() %>%
add_model(random_forest_model) %>%
add_recipe(NVDA_recipe)
random_forest_wflow_INTC <- workflow() %>%
add_model(random_forest_model) %>%
add_recipe(INTC_recipe)
random_forest_wflow_TSM <- workflow() %>%
add_model(random_forest_model) %>%
add_recipe(TSM_recipe)
############################
# Set Up Workflows for Boosted Trees
############################
boosted_trees_wflow_AMD <- workflow() %>%
add_model(boosted_trees_model) %>%
add_recipe(AMD_recipe)
boosted_trees_wflow_NVDA <- workflow() %>%
add_model(boosted_trees_model) %>%
add_recipe(NVDA_recipe)
boosted_trees_wflow_INTC <- workflow() %>%
add_model(boosted_trees_model) %>%
add_recipe(INTC_recipe)
boosted_trees_wflow_TSM <- workflow() %>%
add_model(boosted_trees_model) %>%
add_recipe(TSM_recipe)Tuning Hyperparameters
The fact that the majority of our models have additional parameters (i.e. \(k\) for \(k\)-nearest neighbors, \(\alpha\) for elastic net) is a bit of a double-edged sword: it adds a significant amount of flexibility to our models, but now begs the question of “what values of these additional parameters gives us the best results?” Unfortunately there is often no universal answer which applies to all data-sets. As a result, finding optimal hyperparameter values often feels like navigating through a dark room with our bare hands — we don’t really know where to start, so we blindly go forward until we stumble on something. Though this is a bit of an exaggeration (as there are mathematically rigorous estimates, such as searching near \(k = \sqrt{\# \text{samples}}\) for \(k\)-nearest neighbors), it can certainly feel like the case when a considerable portion of our computational runtime comes from throwing grid-search at each model.
The upshot of tuning hyperparameters is that it is quite easy to understand: we naïvely just punch in a bunch of values (within some predetermined interval) for each hyperparameter and see what happens with that model. In a certain sense, the difficult part is figuring out what interval gives us enough room for an optimized model without turning the computational complexity into a travelling-salesman problem. For both our regression and \(k\)-nearest neighbors, we simply iterate over the default values (spaced by a log base-10 scaling); for random forest and boosted trees, a bit of trial-and-error was required to see which hyperparameters led to a decrease in training root mean square error (RMSE).
############################
# Define Grid for Ridge AND Lasso Regression
############################
lambda_grid <- grid_regular(penalty( range=c(0,1500), trans = identity_trans() ),
levels = 100)
############################
# Define Grid for Elastic Net
############################
elastic_net_grid <- grid_regular(penalty(range=c(0,1500), trans = identity_trans()), mixture(),
levels = 21)
############################
# Define Model for k-Nearest Neighbors
############################
knn_grid <- grid_regular(neighbors(range=c(1,145)),
levels = 145)
############################
# Define Model for Random Forest
############################
random_forest_grid <- grid_regular(mtry(range = c(1, 14)),
trees(range = c(200,700)),
min_n(range = c(1,14)),
levels = 5)
############################
# Define Model for Boosted Trees
############################
boosted_trees_grid <- grid_regular(mtry(range = c(1, 8)),
trees(range = c(200, 700)),
learn_rate(range = c(-5, -0.5)),
levels = 5)The most computationally-intensive part of this entire analysis is
searching for the optimal hyperparameter values within the grids we have
defined above. In order to run this computation as few times as
possible, we save our results to 24 different .rda files
using the built-in save function and instead try to
reference them via load.
############################
# Optimize parameters for Ridge Regression
############################
ridge_tune_AMD <- tune_grid(
ridge_wflow_AMD,
resamples = AMD_folds,
grid = lambda_grid
)
ridge_tune_NVDA <- tune_grid(
ridge_wflow_NVDA,
resamples = NVDA_folds,
grid = lambda_grid
)
ridge_tune_INTC <- tune_grid(
ridge_wflow_INTC,
resamples = INTC_folds,
grid = lambda_grid
)
ridge_tune_TSM <- tune_grid(
ridge_wflow_TSM,
resamples = TSM_folds,
grid = lambda_grid
)
save(ridge_tune_AMD, file = "tuned_grids/ridge_tune_AMD.rda")
save(ridge_tune_NVDA, file = "tuned_grids/ridge_tune_NVDA.rda")
save(ridge_tune_INTC, file = "tuned_grids/ridge_tune_INTC.rda")
save(ridge_tune_TSM, file = "tuned_grids/ridge_tune_TSM.rda")
############################
# Optimize parameters for Lasso Regression
############################
lasso_tune_AMD <- tune_grid(
lasso_wflow_AMD ,
resamples = AMD_folds,
grid = lambda_grid
)
lasso_tune_NVDA <- tune_grid(
lasso_wflow_NVDA ,
resamples = NVDA_folds,
grid = lambda_grid
)
lasso_tune_INTC <- tune_grid(
lasso_wflow_INTC ,
resamples = INTC_folds,
grid = lambda_grid
)
lasso_tune_TSM <- tune_grid(
lasso_wflow_TSM ,
resamples = TSM_folds,
grid = lambda_grid
)
save(lasso_tune_AMD, file = "tuned_grids/lasso_tune_AMD.rda")
save(lasso_tune_NVDA, file = "tuned_grids/lasso_tune_NVDA.rda")
save(lasso_tune_INTC, file = "tuned_grids/lasso_tune_INTC.rda")
save(lasso_tune_TSM, file = "tuned_grids/lasso_tune_TSM.rda")
############################
# Optimize parameters for Elastic Net
############################
elastic_net_tune_AMD <- tune_grid(
elastic_net_wflow_AMD ,
resamples = AMD_folds,
grid = elastic_net_grid
)
elastic_net_tune_NVDA <- tune_grid(
elastic_net_wflow_NVDA ,
resamples = NVDA_folds,
grid = elastic_net_grid
)
elastic_net_tune_INTC <- tune_grid(
elastic_net_wflow_INTC ,
resamples = INTC_folds,
grid = elastic_net_grid
)
elastic_net_tune_TSM <- tune_grid(
elastic_net_wflow_TSM ,
resamples = TSM_folds,
grid = elastic_net_grid
)
save(elastic_net_tune_AMD, file = "tuned_grids/elastic_net_tune_AMD.rda")
save(elastic_net_tune_NVDA, file = "tuned_grids/elastic_net_tune_NVDA.rda")
save(elastic_net_tune_INTC, file = "tuned_grids/elastic_net_tune_INTC.rda")
save(elastic_net_tune_TSM, file = "tuned_grids/elastic_net_tune_TSM.rda")
############################
# Optimize parameters for k-Nearest Neighbors
############################
knn_tune_AMD <- tune_grid(
knn_wflow_AMD ,
resamples = AMD_folds,
grid = knn_grid
)
knn_tune_NVDA <- tune_grid(
knn_wflow_NVDA ,
resamples = NVDA_folds,
grid = knn_grid
)
knn_tune_INTC <- tune_grid(
knn_wflow_INTC ,
resamples = INTC_folds,
grid = knn_grid
)
knn_tune_TSM <- tune_grid(
knn_wflow_TSM ,
resamples = TSM_folds,
grid = knn_grid
)
save(knn_tune_AMD, file = "tuned_grids/knn_tune_AMD.rda")
save(knn_tune_NVDA, file = "tuned_grids/knn_tune_NVDA.rda")
save(knn_tune_INTC, file = "tuned_grids/knn_tune_INTC.rda")
save(knn_tune_TSM, file = "tuned_grids/knn_tune_TSM.rda")
############################
# Optimize parameters for Random Forest
############################
random_forest_tune_AMD <- tune_grid(
random_forest_wflow_AMD,
resamples = AMD_folds,
grid = random_forest_grid
)
random_forest_tune_NVDA <- tune_grid(
random_forest_wflow_NVDA,
resamples = NVDA_folds,
grid = random_forest_grid
)
random_forest_tune_INTC <- tune_grid(
random_forest_wflow_INTC,
resamples = INTC_folds,
grid = random_forest_grid
)
random_forest_tune_TSM <- tune_grid(
random_forest_wflow_TSM,
resamples = TSM_folds,
grid = random_forest_grid
)
save(random_forest_tune_AMD, file = "tuned_grids/random_forest_tune_AMD.rda")
save(random_forest_tune_NVDA, file = "tuned_grids/random_forest_tune_NVDA.rda")
save(random_forest_tune_INTC, file = "tuned_grids/random_forest_tune_INTC.rda")
save(random_forest_tune_TSM, file = "tuned_grids/random_forest_tune_TSM.rda")
############################
# Optimize parameters for Boosted Trees
############################
boosted_trees_tune_AMD <- tune_grid(
boosted_trees_wflow_AMD,
resamples = AMD_folds,
grid = boosted_trees_grid
)
boosted_trees_tune_NVDA <- tune_grid(
boosted_trees_wflow_NVDA,
resamples = NVDA_folds,
grid = boosted_trees_grid
)
boosted_trees_tune_INTC <- tune_grid(
boosted_trees_wflow_INTC,
resamples = INTC_folds,
grid = boosted_trees_grid
)
boosted_trees_tune_TSM <- tune_grid(
boosted_trees_wflow_TSM,
resamples = TSM_folds,
grid = boosted_trees_grid
)
save(boosted_trees_tune_AMD, file = "tuned_grids/boosted_trees_tune_AMD.rda")
save(boosted_trees_tune_NVDA, file = "tuned_grids/boosted_trees_tune_NVDA.rda")
save(boosted_trees_tune_INTC, file = "tuned_grids/boosted_trees_tune_INTC.rda")
save(boosted_trees_tune_TSM, file = "tuned_grids/boosted_trees_tune_TSM.rda")One benefit of running such a broad search is that we gather much more information on our models’ dependencies on their hyperparameters than theoretically would be needed to find an optimal model. Regardless, with this information in hand (for each model across all four stocks) we are able to visualize how the accuracy of our models changes as we vary the value of these parameters:
# Load tuned Ridge Regression grids
load("tuned_grids/ridge_tune_AMD.rda")
load("tuned_grids/ridge_tune_NVDA.rda")
load("tuned_grids/ridge_tune_INTC.rda")
load("tuned_grids/ridge_tune_TSM.rda")
g1<-autoplot(ridge_tune_AMD) + ggtitle("Ridge Regression AMD") + theme_dark()
g2<-autoplot(ridge_tune_NVDA) + ggtitle("Ridge Regression NVDA") + theme_dark()
g3<-autoplot(ridge_tune_INTC) + ggtitle("Ridge Regression INTC") + theme_dark()
g4<-autoplot(ridge_tune_TSM) + ggtitle("Ridge Regression TSM") + theme_dark()
wrapper <- swipeRwrapper(
tags$div(style = "width: 700px; margin: auto;", ggplotly(g1)),
tags$div(style = "width: 700px; margin: auto;", ggplotly(g2)),
tags$div(style = "width: 700px; margin: auto;", ggplotly(g3)),
tags$div(style = "width: 700px; margin: auto;", ggplotly(g4))
)
swipeR(wrapper, height = "400px", navigationColor = "navy")For our ridge regression models, it’s difficult to get an accurate
picture of how the root mean square error (RMSE) depends on the
regularization until the amount of regularization is taken to be
significantly large — in fact, we hand to change the range of the
penalty() parameter in our grid from \(0 \leq \lambda \leq 5\) to \(0 \leq \lambda \leq 1500\) to even obtain
any noticeable affect for the NVIDIA stock. However, this is likely due
to the fact that the closing price (and thus the regression
coefficients) of NVDA are significantly larger than the other three
stocks, meaning a larger value of \(\lambda\) is needed for the \(\lambda \sum_{i=1}^p \beta_i^2\) term to
dominate the modified \(\operatorname{RSS}_{\text{Ridge}}\).
Though modifying our grid search for the purpose of visualizing the
trend between regularization and RMSE means we will not have as accurate
of a “best model” resulting from the grid search, it will ultimately not
affect our process of selecting the best two models for our testing data
(trial and error across a variety of parameters for
grid_regular show that elastic search and regular linear
regression effectively always perform better).
# Load tuned Lasso Regression grids
load("tuned_grids/lasso_tune_AMD.rda")
load("tuned_grids/lasso_tune_NVDA.rda")
load("tuned_grids/lasso_tune_INTC.rda")
load("tuned_grids/lasso_tune_TSM.rda")
g1<-autoplot(lasso_tune_AMD) + ggtitle("Lasso Regression AMD") + theme_dark()
g2<-autoplot(lasso_tune_NVDA) + ggtitle("Lasso Regression NVDA") + theme_dark()
g3<-autoplot(lasso_tune_INTC) + ggtitle("Lasso Regression INTC") + theme_dark()
g4<-autoplot(lasso_tune_TSM) + ggtitle("Lasso Regression TSM") + theme_dark()
wrapper <- swipeRwrapper(
tags$div(style = "width: 700px; margin: auto;", ggplotly(g1)),
tags$div(style = "width: 700px; margin: auto;", ggplotly(g2)),
tags$div(style = "width: 700px; margin: auto;", ggplotly(g3)),
tags$div(style = "width: 700px; margin: auto;", ggplotly(g4))
)
swipeR(wrapper, height = "400px", navigationColor = "navy")While the difference between the penalty terms of lasso and ridge may have seemed negligible at first, we see from the above plots that the \(L^1\) norm has a significantly larger impact on the penalty term of our regression models than the \(L^2\) norm does. In particular, the \(L^1\) error terms of lasso seems to be significantly more sensitive to increases in our regularization parameter \(\lambda\), and seems to approach a maximal RMSE value significantly quicker than ridge regression does.
# Load tuned Elastic Net grids
load("tuned_grids/elastic_net_tune_AMD.rda")
load("tuned_grids/elastic_net_tune_NVDA.rda")
load("tuned_grids/elastic_net_tune_INTC.rda")
load("tuned_grids/elastic_net_tune_TSM.rda")
g1<-autoplot(elastic_net_tune_AMD) + ggtitle("Elastic Net AMD") + theme_dark()
g2<-autoplot(elastic_net_tune_NVDA) + ggtitle("Elastic Net NVDA") + theme_dark()
g3<-autoplot(elastic_net_tune_INTC) + ggtitle("Elastic Net INTC") + theme_dark()
g4<-autoplot(elastic_net_tune_TSM) + ggtitle("Elastic Net TSM") + theme_dark()
wrapper <- swipeRwrapper(
tags$div(style = "width: 700px; margin: auto;", ggplotly(g1)),
tags$div(style = "width: 700px; margin: auto;", ggplotly(g2)),
tags$div(style = "width: 700px; margin: auto;", ggplotly(g3)),
tags$div(style = "width: 700px; margin: auto;", ggplotly(g4))
)
swipeR(wrapper, height = "400px", navigationColor = "navy")We are also now able to visualize quite plainly how elastic net interpolates the penalty terms of ridge and lasso regression. For example, the blue/purple/pink curves displayed on the NVDA plot behave effectively the same as the lasso regression since the RMSE quickly increases around \(\lambda = 100\) towards the same asymptotic value.
# Load tuned k-Nearest Neighbors grids
load("tuned_grids/knn_tune_AMD.rda")
load("tuned_grids/knn_tune_NVDA.rda")
load("tuned_grids/knn_tune_INTC.rda")
load("tuned_grids/knn_tune_TSM.rda")
g1<-autoplot(knn_tune_AMD) + ggtitle("k-Nearest Neighbors AMD") + theme_dark()
g2<-autoplot(knn_tune_NVDA) + ggtitle("k-Nearest Neighbors NVDA") + theme_dark()
g3<-autoplot(knn_tune_INTC) + ggtitle("k-Nearest Neighbors INTC") + theme_dark()
g4<-autoplot(knn_tune_TSM) + ggtitle("k-Nearest Neighbors TSM") + theme_dark()
wrapper <- swipeRwrapper(
tags$div(style = "width: 700px; margin: auto;", ggplotly(g1)),
tags$div(style = "width: 700px; margin: auto;", ggplotly(g2)),
tags$div(style = "width: 700px; margin: auto;", ggplotly(g3)),
tags$div(style = "width: 700px; margin: auto;", ggplotly(g4))
)
swipeR(wrapper, height = "400px", navigationColor = "navy")For \(k\)-Nearest neighbors, we additionally see that the optimal choices of \(k\) (i.e. the local minima on the graphs above) do occur somewhat close to the square root of the number of sample sizes (i.e. roughly 15 in this case). However, the positive relationship between \(k\) and our RMSE value shows smaller values of \(k\) are still favored — in fact, the optimal values of \(k\) vary between \(2 \leq k \leq 6\) for all four of our stocks.
# Load tuned Random Forest grids
load("tuned_grids/random_forest_tune_AMD.rda")
load("tuned_grids/random_forest_tune_NVDA.rda")
load("tuned_grids/random_forest_tune_INTC.rda")
load("tuned_grids/random_forest_tune_TSM.rda")
g1<-autoplot(random_forest_tune_AMD) + ggtitle("Random Forest AMD") + theme_dark() + theme(text = element_text(size = 9))
g2<-autoplot(random_forest_tune_NVDA) + ggtitle("Random Forest NVDA") + theme_dark() + theme(text = element_text(size = 9))
g3<-autoplot(random_forest_tune_INTC) + ggtitle("Random Forest INTC") + theme_dark() + theme(text = element_text(size = 9))
g4<-autoplot(random_forest_tune_TSM) + ggtitle("Random Forest TSM") + theme_dark() + theme(text = element_text(size = 9))# Load tuned Boosted Trees grids
load("tuned_grids/boosted_trees_tune_AMD.rda")
load("tuned_grids/boosted_trees_tune_NVDA.rda")
load("tuned_grids/boosted_trees_tune_INTC.rda")
load("tuned_grids/boosted_trees_tune_TSM.rda")
autoplot(boosted_trees_tune_AMD) + theme_dark() + ggtitle("Boosted Trees AMD") + theme(text = element_text(size = 9))
autoplot(boosted_trees_tune_NVDA) + theme_dark() + ggtitle("Boosted Trees NVDA") + theme(text = element_text(size = 9))
Though having a vast amount of data about the relationship between
our hyperparameters and training accuracy is useful for better
understanding each model, we ultimately only care about which exact
hyperparameter values give us the best results (according to some chosen
metrics). Using the select_best() function from our
tidymodels package, we need only specify the metric we care
most about in order to extract the optimal model for each of our seven
statistical learning algorithms, thus concluding the hyperparameter
tuning portion of this analysis.
############################
# Select best model(s) for Ridge Regression
############################
ridge_final_wflow_AMD <- select_best(ridge_tune_AMD , metric="rmse" ) %>%
finalize_workflow(x=ridge_wflow_AMD )
ridge_final_wflow_NVDA <- select_best(ridge_tune_NVDA , metric="rmse" ) %>%
finalize_workflow(x=ridge_wflow_NVDA )
ridge_final_wflow_INTC <- select_best(ridge_tune_INTC , metric="rmse" ) %>%
finalize_workflow(x=ridge_wflow_INTC )
ridge_final_wflow_TSM <- select_best(ridge_tune_TSM , metric="rmse" ) %>%
finalize_workflow(x=ridge_wflow_TSM )
############################
# Select best model(s) for Lasso Regression
############################
lasso_final_wflow_AMD <- select_best(lasso_tune_AMD , metric="rmse") %>%
finalize_workflow(x=lasso_wflow_AMD )
lasso_final_wflow_NVDA <- select_best(lasso_tune_NVDA , metric="rmse") %>%
finalize_workflow(x=lasso_wflow_NVDA )
lasso_final_wflow_INTC <- select_best(lasso_tune_INTC , metric="rmse") %>%
finalize_workflow(x=lasso_wflow_INTC )
lasso_final_wflow_TSM <- select_best(lasso_tune_TSM , metric="rmse") %>%
finalize_workflow(x=lasso_wflow_TSM )
############################
# Select best model(s) for Elastic Net
############################
elastic_net_final_wflow_AMD <- select_best(elastic_net_tune_AMD , metric = "rmse") %>%
finalize_workflow(x=elastic_net_wflow_AMD )
elastic_net_final_wflow_NVDA <- select_best(elastic_net_tune_NVDA , metric = "rmse") %>%
finalize_workflow(x=elastic_net_wflow_NVDA )
elastic_net_final_wflow_INTC <- select_best(elastic_net_tune_INTC , metric = "rmse") %>%
finalize_workflow(x=elastic_net_wflow_INTC )
elastic_net_final_wflow_TSM <- select_best(elastic_net_tune_TSM , metric = "rmse") %>%
finalize_workflow(x=elastic_net_wflow_TSM )
############################
# Select best model(s) for k-Nearest Neighbors
############################
knn_final_wflow_AMD <- select_best(knn_tune_AMD , metric = "rmse") %>%
finalize_workflow(x=knn_wflow_AMD )
knn_final_wflow_NVDA <- select_best(knn_tune_NVDA , metric = "rmse") %>%
finalize_workflow(x=knn_wflow_NVDA )
knn_final_wflow_INTC <- select_best(knn_tune_INTC , metric = "rmse") %>%
finalize_workflow(x=knn_wflow_INTC )
knn_final_wflow_TSM <- select_best(knn_tune_TSM , metric = "rmse") %>%
finalize_workflow(x=knn_wflow_TSM )
############################
# Select best model(s) for Random Forest
############################
random_forest_final_wflow_AMD <- select_best(random_forest_tune_AMD , metric = "rmse") %>%
finalize_workflow(x=random_forest_wflow_AMD )
random_forest_final_wflow_NVDA <- select_best(random_forest_tune_NVDA , metric = "rmse") %>%
finalize_workflow(x=random_forest_wflow_NVDA )
random_forest_final_wflow_INTC <- select_best(random_forest_tune_INTC , metric = "rmse") %>%
finalize_workflow(x=random_forest_wflow_INTC )
random_forest_final_wflow_TSM <- select_best(random_forest_tune_TSM , metric = "rmse") %>%
finalize_workflow(x=random_forest_wflow_TSM )
############################
# Select best model(s) for Boosted Trees
############################
boosted_trees_final_wflow_AMD <- select_best(boosted_trees_tune_AMD , metric = "rmse") %>%
finalize_workflow(x=boosted_trees_wflow_AMD )
boosted_trees_final_wflow_NVDA <- select_best(boosted_trees_tune_NVDA , metric = "rmse") %>%
finalize_workflow(x=boosted_trees_wflow_NVDA )
boosted_trees_final_wflow_INTC <- select_best(boosted_trees_tune_INTC , metric = "rmse") %>%
finalize_workflow(x=boosted_trees_wflow_INTC )
boosted_trees_final_wflow_TSM <- select_best(boosted_trees_tune_TSM , metric = "rmse") %>%
finalize_workflow(x=boosted_trees_wflow_TSM )Performance of Models
After an arduous process of tuning each of our seven models across all four stocks, we now have an array of workflows that should be well-primed for our entire set of training data (remember that the hyperparameter tuning was done on 10-fold cross validation, so each model has only seen \(9/10^{th}\) of the data ). Ultimately, the performance of our models on the the entire training set (exactly \(1/10^{th}\) of which is unseen by the models) need not give any indication about the performance of these models on unseen testing data; regardless, we will use this as a benchmark for which two models pass on to the testing data.
############################
# Fit model(s) for Linear Regression
############################
linear_fit_AMD <- fit(linear_wflow_AMD, AMD_train)
linear_fit_NVDA <- fit(linear_wflow_NVDA, NVDA_train)
linear_fit_INTC <- fit(linear_wflow_INTC, INTC_train)
linear_fit_TSM <- fit(linear_wflow_TSM, TSM_train)
############################
# Fit model(s) for Ridge Regression
############################
ridge_fit_AMD <- fit(ridge_final_wflow_AMD , AMD_train)
ridge_fit_NVDA <- fit(ridge_final_wflow_NVDA , NVDA_train)
ridge_fit_INTC <- fit(ridge_final_wflow_INTC , INTC_train)
ridge_fit_TSM <- fit(ridge_final_wflow_TSM , TSM_train)
############################
# Fit model(s) for Lasso Regression
############################
lasso_fit_AMD <- fit(lasso_final_wflow_AMD , AMD_train)
lasso_fit_NVDA <- fit(lasso_final_wflow_NVDA , NVDA_train)
lasso_fit_INTC <- fit(lasso_final_wflow_INTC , INTC_train)
lasso_fit_TSM <- fit(lasso_final_wflow_TSM , TSM_train)
############################
# Fit model(s) for Elastic Net
############################
elastic_net_fit_AMD <- fit(elastic_net_final_wflow_AMD , AMD_train)
elastic_net_fit_NVDA <- fit(elastic_net_final_wflow_NVDA , NVDA_train)
elastic_net_fit_INTC <- fit(elastic_net_final_wflow_INTC , INTC_train)
elastic_net_fit_TSM <- fit(elastic_net_final_wflow_TSM , TSM_train)
############################
# Fit model(s) for k-Nearest Neighbors
############################
knn_fit_AMD <- fit(knn_final_wflow_AMD , AMD_train)
knn_fit_NVDA <- fit(knn_final_wflow_NVDA , NVDA_train)
knn_fit_INTC <- fit(knn_final_wflow_INTC , INTC_train)
knn_fit_TSM <- fit(knn_final_wflow_TSM , TSM_train)
############################
# Fit model(s) for Random Forest
############################
random_forest_fit_AMD <- fit(random_forest_final_wflow_AMD , AMD_train)
random_forest_fit_NVDA <- fit(random_forest_final_wflow_NVDA , NVDA_train)
random_forest_fit_TSM <- fit(random_forest_final_wflow_TSM, TSM_train)
random_forest_fit_INTC <- fit(random_forest_final_wflow_INTC, INTC_train)
############################
# Fit model(s) for Boosted Trees
############################
boosted_trees_fit_AMD <- fit(boosted_trees_final_wflow_AMD , AMD_train)
boosted_trees_fit_NVDA <- fit(boosted_trees_final_wflow_NVDA , NVDA_train)
boosted_trees_fit_TSM <- fit(boosted_trees_final_wflow_TSM, TSM_train)
boosted_trees_fit_INTC <- fit(boosted_trees_final_wflow_INTC, INTC_train)Variable Importance
As a brief aside, with the models fit we now have all the information
necessary to examine how much impact each predictor has. Although this
step would have been much more valuable prior to the recipe creation
(though this is a bit of a Catch-22 since a recipe, model, and fit is
required to obtain the results), the vip library contains
an incredibly useful tool to extract information from our tree-base
statistical learning algorithms regarding which variables affect the
outcome of our model most heavily. For example, we can plot the
variable importance plots for the AMD stock according to both
the random forest algorithm and the boosted trees algorithm to see that
there is actually a slight difference in terms of which variable is
considered most relevant for predicting stock price:
random_forest_fit_AMD %>% extract_fit_parsnip() %>%
vip()
boosted_trees_fit_AMD %>% extract_fit_parsnip() %>%
vip() 
It should be noted that the horizontal axis scaling should be ignored since we are comparing the variable importance plots (VIPs) between two distinct tree algorithms. Though there is a slight difference between the ordering of the variables, we still see that the 1-day lag variable (i.e. the information from the previous day) is still one of the most important pieces of information between the two algorithms. This may lead one to believe that ‘short-term’ lag variables will always take precedence over ‘long-term’ variables in these time-series based problems — however, by looking at the VIPs for NVDA below we see that the first lag variable doesn’t even appear in the top 50% for either algorithm.
random_forest_fit_NVDA %>% extract_fit_parsnip() %>%
vip()
boosted_trees_fit_NVDA %>% extract_fit_parsnip() %>%
vip() 
When the outcome of a time-series depends much more heavily on long-term lag variables (say of lag-length \(N \gg 1\)) than more recent lag variables, this can indicate periodicity or seasonality in our data. It turns out that there are several packages in R that attempt to identify seasonality through basic Fourier analysis — however, for the sake of this project we will not attempt to rectify several of these pre-processing issues that arise in time series instead of statistical learning as a whole. It is also worth considering the case that the periodicity of the NVDA stock (i.e. dependence on 6-7 day old data) may simply be a feature of our sample of data-set. For example, the importance of variables for our INTC and TSM stocks seem to be much closer to what a typical investor would choose to scrutinize in order to predict a stocks closing price.
g1<-random_forest_fit_INTC %>% extract_fit_parsnip() %>%
vip() + ggtitle("(ITNC) Random Forest VIP")
g2<-boosted_trees_fit_INTC %>% extract_fit_parsnip() %>%
vip() + ggtitle("(ITNC) Boosted Trees VIP")
g3<-random_forest_fit_TSM %>% extract_fit_parsnip() %>%
vip() + ggtitle("(TSM) Random Forest VIP")
g4<-boosted_trees_fit_TSM %>% extract_fit_parsnip() %>%
vip() + ggtitle("(TSM) Random Forest VIP")
wrapper <- swipeRwrapper(
tags$div(style = "width: 700px; margin: auto;", ggplotly(g1)),
tags$div(style = "width: 700px; margin: auto;", ggplotly(g2)),
tags$div(style = "width: 700px; margin: auto;", ggplotly(g3)),
tags$div(style = "width: 700px; margin: auto;", ggplotly(g4))
)
swipeR(wrapper, height = "400px", navigationColor = "navy")Training Accuracy
Returning to the topic of our models’ performance, in order to gather any sort of insight about the accuracy of our models we must first keep track of the difference between their predictions and the actual values of labelled data. While there are likely better ways to store the results so that labelled training data isn’t getting replicated in memory 7 times (for each stock, in fact), the most convenient way to keep track of these differences is simply attaching a \(2 \times (\#\ \text{of}\ \text{training}\ \text{observations})\) matrix to each model:
############################
# Use Linear Regression Fit to Predict Training Data
############################
linear_train_res_AMD <- predict(linear_fit_AMD, new_data = AMD_train %>% select(-Close)) %>%
bind_cols(AMD_train %>% select(Close))
linear_train_res_NVDA <- predict(linear_fit_NVDA, new_data = NVDA_train %>% select(-Close)) %>%
bind_cols(NVDA_train %>% select(Close))
linear_train_res_INTC <- predict(linear_fit_INTC, new_data = INTC_train %>% select(-Close)) %>%
bind_cols(INTC_train %>% select(Close))
linear_train_res_TSM <- predict(linear_fit_TSM, new_data = TSM_train %>% select(-Close)) %>%
bind_cols(TSM_train %>% select(Close))
############################
# Use Ridge Regression Fit to Predict Training Data
############################
ridge_train_res_AMD <- predict(ridge_fit_AMD , new_data = AMD_train %>% select(-Close)) %>%
bind_cols(AMD_train %>% select(Close))
ridge_train_res_NVDA <- predict(ridge_fit_NVDA , new_data = NVDA_train %>% select(-Close)) %>%
bind_cols(NVDA_train %>% select(Close))
ridge_train_res_INTC <- predict(ridge_fit_INTC , new_data = INTC_train %>% select(-Close)) %>%
bind_cols(INTC_train %>% select(Close))
ridge_train_res_TSM <- predict(ridge_fit_TSM , new_data = TSM_train %>% select(-Close)) %>%
bind_cols(TSM_train %>% select(Close))
############################
# Use Lasso Regression Fit to Predict Training Data
############################
lasso_train_res_AMD <- predict(lasso_fit_AMD , new_data = AMD_train %>% select(-Close)) %>%
bind_cols(AMD_train %>% select(Close))
lasso_train_res_NVDA <- predict(lasso_fit_NVDA , new_data = NVDA_train %>% select(-Close)) %>%
bind_cols(NVDA_train %>% select(Close))
lasso_train_res_INTC <- predict(lasso_fit_INTC , new_data = INTC_train %>% select(-Close)) %>%
bind_cols(INTC_train %>% select(Close))
lasso_train_res_TSM <- predict(lasso_fit_TSM , new_data = TSM_train %>% select(-Close)) %>%
bind_cols(TSM_train %>% select(Close))
############################
# Use Elastic Net Fit to Predict Training Data
############################
elastic_net_train_res_AMD <- predict(elastic_net_fit_AMD , new_data = AMD_train %>% select(-Close )) %>%
bind_cols(AMD_train %>% select(Close ))
elastic_net_train_res_NVDA <- predict(elastic_net_fit_NVDA , new_data = NVDA_train %>% select(-Close )) %>%
bind_cols(NVDA_train %>% select(Close ))
elastic_net_train_res_INTC <- predict(elastic_net_fit_INTC , new_data = INTC_train %>% select(-Close )) %>%
bind_cols(INTC_train %>% select(Close ))
elastic_net_train_res_TSM <- predict(elastic_net_fit_TSM , new_data = TSM_train %>% select(-Close )) %>%
bind_cols(TSM_train %>% select(Close ))
############################
# Use k-Nearest Neighbors Fit to Predict Training Data
############################
knn_train_res_AMD <- predict(knn_fit_AMD , new_data = AMD_train %>% select(-Close )) %>%
bind_cols(AMD_train %>% select(Close ))
knn_train_res_NVDA <- predict(knn_fit_NVDA , new_data = NVDA_train %>% select(-Close )) %>%
bind_cols(NVDA_train %>% select(Close ))
knn_train_res_INTC <- predict(knn_fit_INTC , new_data = INTC_train %>% select(-Close )) %>%
bind_cols(INTC_train %>% select(Close ))
knn_train_res_TSM <- predict(knn_fit_TSM , new_data = TSM_train %>% select(-Close )) %>%
bind_cols(TSM_train %>% select(Close ))
############################
# Use Random Forest Fit to Predict Training Data
############################
random_forest_train_res_AMD <- predict(random_forest_fit_AMD , new_data = AMD_train %>% select(-Close )) %>%
bind_cols(AMD_train %>% select(Close ))
random_forest_train_res_NVDA <- predict(random_forest_fit_NVDA , new_data = NVDA_train %>% select(-Close )) %>%
bind_cols(NVDA_train %>% select(Close ))
random_forest_train_res_INTC <- predict(random_forest_fit_INTC , new_data = INTC_train %>% select(-Close )) %>%
bind_cols(INTC_train %>% select(Close ))
random_forest_train_res_TSM <- predict(random_forest_fit_TSM , new_data = TSM_train %>% select(-Close )) %>%
bind_cols(TSM_train %>% select(Close ))
############################
# Use Booested Trees Fit to Predict Training Data
############################
boosted_trees_train_res_AMD <- predict(boosted_trees_fit_AMD , new_data = AMD_train %>% select(-Close )) %>%
bind_cols(AMD_train %>% select(Close ))
boosted_trees_train_res_NVDA <- predict(boosted_trees_fit_NVDA , new_data = NVDA_train %>% select(-Close )) %>%
bind_cols(NVDA_train %>% select(Close ))
boosted_trees_train_res_INTC <- predict(boosted_trees_fit_INTC , new_data = INTC_train %>% select(-Close )) %>%
bind_cols(INTC_train %>% select(Close ))
boosted_trees_train_res_TSM <- predict(boosted_trees_fit_TSM , new_data = TSM_train %>% select(-Close )) %>%
bind_cols(TSM_train %>% select(Close ))Using the yardstick package, we are then able to feed
each model’s matrix of predicted versus actual outcomes through the
rmse() function to create a succinct table of the root mean
squared error on the training data:
tibble(Model = c("Linear Regression", "Ridge Regression", "Lasso Regression", "Elastic Net", "k-Nearest Neighbors", "Random Forest", "Boosted Trees"),
AMD = c((linear_train_res_AMD %>% rmse( Close, .pred))$.estimate,
(ridge_train_res_AMD %>% rmse( Close, .pred))$.estimate,
(lasso_train_res_AMD %>% rmse( Close, .pred))$.estimate,
(elastic_net_train_res_AMD %>% rmse( Close, .pred))$.estimate,
(knn_train_res_AMD %>% rmse( Close, .pred))$.estimate,
(random_forest_train_res_AMD %>% rmse( Close, .pred))$.estimate,
(boosted_trees_train_res_AMD %>% rmse( Close, .pred))$.estimate),
NVDA = c((linear_train_res_NVDA %>% rmse( Close, .pred))$.estimate,
(ridge_train_res_NVDA %>% rmse( Close, .pred))$.estimate,
(lasso_train_res_NVDA %>% rmse( Close, .pred))$.estimate,
(elastic_net_train_res_NVDA %>% rmse( Close, .pred))$.estimate,
(knn_train_res_NVDA %>% rmse( Close, .pred))$.estimate,
(random_forest_train_res_NVDA %>% rmse( Close, .pred))$.estimate,
(boosted_trees_train_res_NVDA %>% rmse( Close, .pred))$.estimate),
INTC = c((linear_train_res_INTC %>% rmse( Close, .pred))$.estimate,
(ridge_train_res_INTC %>% rmse( Close, .pred))$.estimate,
(lasso_train_res_INTC %>% rmse( Close, .pred))$.estimate,
(elastic_net_train_res_INTC %>% rmse( Close, .pred))$.estimate,
(knn_train_res_INTC %>% rmse( Close, .pred))$.estimate,
(random_forest_train_res_INTC %>% rmse( Close, .pred))$.estimate,
(boosted_trees_train_res_INTC %>% rmse( Close, .pred))$.estimate),
TSM = c((linear_train_res_TSM %>% rmse( Close, .pred))$.estimate,
(ridge_train_res_TSM %>% rmse( Close, .pred))$.estimate,
(lasso_train_res_TSM %>% rmse( Close, .pred))$.estimate,
(elastic_net_train_res_TSM %>% rmse( Close, .pred))$.estimate,
(knn_train_res_TSM %>% rmse( Close, .pred))$.estimate,
(random_forest_train_res_TSM %>% rmse( Close, .pred))$.estimate,
(boosted_trees_train_res_TSM %>% rmse( Close, .pred))$.estimate)
) %>%
kable(caption = "RMSE Performance on Training Data") %>%
kable_styling(full_width = F)| Model | AMD | NVDA | INTC | TSM |
|---|---|---|---|---|
| Linear Regression | 2.871422 | 10.373521 | 0.7935312 | 1.4947979 |
| Ridge Regression | 3.568217 | 14.307980 | 1.0734006 | 1.7217499 |
| Lasso Regression | 2.941790 | 10.567281 | 0.8256462 | 1.4995853 |
| Elastic Net | 2.941790 | 10.567281 | 0.8030887 | 1.4997415 |
| k-Nearest Neighbors | 2.435858 | 5.889763 | 0.5496082 | 0.9186670 |
| Random Forest | 1.347663 | 4.621556 | 0.3676519 | 0.6206633 |
| Boosted Trees | 1.769673 | 1.672893 | 0.0746471 | 0.1787649 |
Though of slightly less importance, we may also pipe the same data through the \(R^2\) metric to see that effectively all of our models accurately describe the variance in the closing price of each stock:
tibble(Model = c("Linear Regression", "Ridge Regression", "Lasso Regression", "Elastic Net", "k-Nearest Neighbors", "Random Forest", "Boosted Trees"),
AMD = c((linear_train_res_AMD %>% rsq( Close, .pred))$.estimate,
(ridge_train_res_AMD %>% rsq( Close, .pred))$.estimate,
(lasso_train_res_AMD %>% rsq( Close, .pred))$.estimate,
(elastic_net_train_res_AMD %>% rsq( Close, .pred))$.estimate,
(knn_train_res_AMD %>% rsq( Close, .pred))$.estimate,
(random_forest_train_res_AMD %>% rsq( Close, .pred))$.estimate,
(boosted_trees_train_res_AMD %>% rsq( Close, .pred))$.estimate),
NVDA = c((linear_train_res_NVDA %>% rsq( Close, .pred))$.estimate,
(ridge_train_res_NVDA %>% rsq( Close, .pred))$.estimate,
(lasso_train_res_NVDA %>% rsq( Close, .pred))$.estimate,
(elastic_net_train_res_NVDA %>% rsq( Close, .pred))$.estimate,
(knn_train_res_NVDA %>% rsq( Close, .pred))$.estimate,
(random_forest_train_res_NVDA %>% rsq( Close, .pred))$.estimate,
(boosted_trees_train_res_NVDA %>% rsq( Close, .pred))$.estimate),
INTC = c((linear_train_res_INTC %>% rsq( Close, .pred))$.estimate,
(ridge_train_res_INTC %>% rsq( Close, .pred))$.estimate,
(lasso_train_res_INTC %>% rsq( Close, .pred))$.estimate,
(elastic_net_train_res_INTC %>% rsq( Close, .pred))$.estimate,
(knn_train_res_INTC %>% rsq( Close, .pred))$.estimate,
(random_forest_train_res_INTC %>% rsq( Close, .pred))$.estimate,
(boosted_trees_train_res_INTC %>% rsq( Close, .pred))$.estimate),
TSM = c((linear_train_res_TSM %>% rsq( Close, .pred))$.estimate,
(ridge_train_res_TSM %>% rsq( Close, .pred))$.estimate,
(lasso_train_res_TSM %>% rsq( Close, .pred))$.estimate,
(elastic_net_train_res_TSM %>% rsq( Close, .pred))$.estimate,
(knn_train_res_TSM %>% rsq( Close, .pred))$.estimate,
(random_forest_train_res_TSM %>% rsq( Close, .pred))$.estimate,
(boosted_trees_train_res_TSM %>% rsq( Close, .pred))$.estimate)
) %>%
kable(caption="R^2 Performance on Training Data") %>%
kable_styling(full_width = F)| Model | AMD | NVDA | INTC | TSM |
|---|---|---|---|---|
| Linear Regression | 0.9739886 | 0.9907645 | 0.9796371 | 0.9457706 |
| Ridge Regression | 0.9601138 | 0.9825690 | 0.9631572 | 0.9289761 |
| Lasso Regression | 0.9727204 | 0.9904317 | 0.9779642 | 0.9454231 |
| Elastic Net | 0.9727204 | 0.9904317 | 0.9791492 | 0.9454125 |
| k-Nearest Neighbors | 0.9818798 | 0.9970261 | 0.9904188 | 0.9800220 |
| Random Forest | 0.9943104 | 0.9981700 | 0.9957915 | 0.9909574 |
| Boosted Trees | 0.9944443 | 0.9997721 | 0.9998244 | 0.9992606 |
Based on the tables above, we see that there is a clear choice for the two best performing models: random forest and boosted trees. Returning to purpose of our initial data split, it is important to double-check that our model actually makes sense when introduced to new data and is not simply over-fitting the training data.
Best Models and Testing Performance
Applying the same rudimentary procedure to our testing data, we store the predicted versus actual outcomes of the testing data in a series of \(2 \times N_{\text{test}}\) matrices (where \(N_{\text{test}}\) is the size of our testing data set).
############################
# Use Linear Regression Fit to Predict Testing Data
############################
random_forest_test_res_AMD <- predict(random_forest_fit_AMD, new_data = AMD_test %>% select(-Close)) %>%
bind_cols(AMD_test %>% select(Close))
random_forest_test_res_NVDA <- predict(random_forest_fit_NVDA, new_data = NVDA_test %>% select(-Close)) %>%
bind_cols(NVDA_test %>% select(Close))
random_forest_test_res_INTC <- predict(random_forest_fit_INTC, new_data = INTC_test %>% select(-Close)) %>%
bind_cols(INTC_test %>% select(Close))
random_forest_test_res_TSM <- predict(random_forest_fit_TSM, new_data = TSM_test %>% select(-Close)) %>%
bind_cols(TSM_test %>% select(Close))
############################
# Use Boosted Trees Fit to Predict Testing Data
############################
boosted_trees_test_res_AMD <- predict(boosted_trees_fit_AMD , new_data = AMD_test %>% select(-Close )) %>%
bind_cols(AMD_test %>% select(Close ))
boosted_trees_test_res_NVDA <- predict(boosted_trees_fit_NVDA , new_data = NVDA_test %>% select(-Close )) %>%
bind_cols(NVDA_test %>% select(Close ))
boosted_trees_test_res_INTC <- predict(boosted_trees_fit_INTC , new_data = INTC_test %>% select(-Close )) %>%
bind_cols(INTC_test %>% select(Close ))
boosted_trees_test_res_TSM <- predict(boosted_trees_fit_TSM , new_data = TSM_test %>% select(-Close )) %>%
bind_cols(TSM_test %>% select(Close ))tibble(Model = c("Random Forest", "Boosted Trees"),
AMD = c((random_forest_test_res_AMD %>% rmse( Close, .pred))$.estimate,
(boosted_trees_test_res_AMD %>% rmse( Close, .pred))$.estimate),
NVDA = c((random_forest_test_res_NVDA %>% rmse( Close, .pred))$.estimate,
(boosted_trees_test_res_NVDA %>% rmse( Close, .pred))$.estimate),
INTC = c((random_forest_test_res_INTC %>% rmse( Close, .pred))$.estimate,
(boosted_trees_test_res_INTC %>% rmse( Close, .pred))$.estimate),
TSM = c((random_forest_test_res_TSM %>% rmse( Close, .pred))$.estimate,
(boosted_trees_test_res_TSM %>% rmse( Close, .pred))$.estimate)
) %>%
kable(caption = "RMSE Performance on Testing Data") %>%
kable_styling(full_width = F)| Model | AMD | NVDA | INTC | TSM |
|---|---|---|---|---|
| Random Forest | 3.042175 | 8.666508 | 0.8770444 | 2.354993 |
| Boosted Trees | 3.349941 | 9.460352 | 0.8884608 | 2.367970 |
Unlike the case of the training data, we see that both models perform with roughly the same accuracy according to our metrics. Fortunately, the fact that the root mean squared error is relatively low across both models also indicates that there is not likely too much over-fitting occurring.
tibble(Model = c("Linear Regression", "Boosted Trees"),
AMD = c((random_forest_test_res_AMD %>% rsq( Close, .pred))$.estimate,
(boosted_trees_test_res_AMD %>% rsq( Close, .pred))$.estimate),
NVDA = c((random_forest_test_res_NVDA %>% rsq( Close, .pred))$.estimate,
(boosted_trees_test_res_NVDA %>% rsq( Close, .pred))$.estimate),
INTC = c((random_forest_test_res_INTC %>% rsq( Close, .pred))$.estimate,
(boosted_trees_test_res_INTC %>% rsq( Close, .pred))$.estimate),
TSM = c((random_forest_test_res_TSM %>% rsq( Close, .pred))$.estimate,
(boosted_trees_test_res_TSM %>% rsq( Close, .pred))$.estimate)
) %>%
kable(caption="R^2 Performance on Testing Data") %>%
kable_styling(full_width = F) | Model | AMD | NVDA | INTC | TSM |
|---|---|---|---|---|
| Linear Regression | 0.9703525 | 0.9939269 | 0.9706446 | 0.8836784 |
| Boosted Trees | 0.9689292 | 0.9925082 | 0.9701453 | 0.8834758 |
Addendum: Short Term Forecasting Accuracy
While several of the non-parametric models introduced performed
surprisingly well in terms of predicting tomorrow value of a stock based
on today’s data, one additional way to measure the performance of a
time-series based problem is how the prediction error
propogates over time. Put differently, what if we predicted
tomorrow’s closing prices for a stock using our model, and then fed that
data into the predict input again to predict the stock
price two days from now (without ever referencing tomorrows actual
data). Applying this idea consecutively, we arrive at the notion of
forecasting in time series analysis, which tries to
minimize thearea between the time-evolution curve of the actual data and
the time-evolution curve of the data points obtained by iteratively
predicting outcomes using previous predictions. This approach turns out
to drastically affect the way in which we measure our models.
Although there are libraries such as forecast which
include methods that handle the functionality we are specifically
looking for, our models are unfortunately not compatible with the
arguments needed — we would have to retrain a handful of the models
using quantmod or other similar packages. Instead, we
choose to hardcode a forecast_comparison function which
attempts to match the structure of our data and models (such as
including all of the “added predictors” from the Exploratory Data
Analysis section).
forecast_comparison <- function(symbol_string, tidy_fit, first_date, num_days = 10, include.historical = FALSE) {
#' Using a tidymodels 'fit' associated to predicting the closing price of some stock, predict the following
#' num_day closing prices based off successive predictions
#'
#' Creates a new column vector whose entries are the predicted price of the closing value of a stock, where
#' each prediction is used to predict successive calls to predict (i.e. the lag variables are also predicted
#' data).
#'
#' @param symbol_string A string literal representing a Stock ticker symbol. Must be recognizable by the quantmod library.
#' @param tidy_fit The output of a call to the tidymodel's function fit(). Implicitly assumed that the model is fit to training
#' data based on the same stock symbol
#' @param first_date The date to begin predicting closing prices; must be a string object of the format "yyyy-mm-dd"
#' @param num_days The number of days for the model to predict. By default, it is set to 10
#' @param include.historical Boolean flag which indicates whether the return variable should also return the previous closing
#' prices prior to first_date used to set the lag variables
#'
#' @return a column vector whose entries are the predicted closing price starting from the first_date
####################
# Rudimentary parameter checking
####################
if(is.na(as.Date(first_date, format="%Y-%m-%d"))){
stop("Incorrect Date Format: first_date must be string of the form yyyy-mm-dd")
}
if(is.numeric(num_days) == FALSE){
stop("Non-numeric Entries: num_days must be a number")
}
if(num_days < 1){
stop("Non-positive nuber of days specified")
}
# Define date ranges to ensure enough sample data is pulled to define all lag variables
# and moving average variables (specifically, at least 20 closing prices are needed in
# order to define MACD / 1-month EMA)
sample_period_size <- 30
data_pull_beginning <- as.Date(first_date) - (sample_period_size+1)
data_pull_end <- as.Date(first_date) - 1
# Pull data from Yahoo Finance
initial_stock_data <- loadSymbols(symbol_string, from=data_pull_beginning, to=data_pull_end, auto.assign = FALSE)
date_vector <- index(initial_stock_data)
# Since quantmod (by default) assigns column names that include the stock symbol, we want
# to remove the stock symbol so that variable names are consistent accross all stocks
initial_stock_data <- data.frame(initial_stock_data) %>% rename(
Open = colnames(initial_stock_data)[1],
High = colnames(initial_stock_data)[2],
Low = colnames(initial_stock_data)[3],
Close = colnames(initial_stock_data)[4],
Volume = colnames(initial_stock_data)[5],
Adjusted = colnames(initial_stock_data)[6]
)
# add date vector
initial_stock_data$Date <- date_vector
###########################
# Set up dataframe for model
###########################
# Add Lag variables for model to use
initial_stock_data$Close_L1 <- lag(initial_stock_data$Close, 1)
initial_stock_data$Close_L2 <- lag(initial_stock_data$Close, 2)
initial_stock_data$Close_L3 <- lag(initial_stock_data$Close, 3)
initial_stock_data$Close_L4 <- lag(initial_stock_data$Close, 4)
initial_stock_data$Close_L5 <- lag(initial_stock_data$Close, 5)
initial_stock_data$Close_L6 <- lag(initial_stock_data$Close, 6)
initial_stock_data$Close_L7 <- lag(initial_stock_data$Close, 7)
initial_stock_data$Close_L8 <- lag(initial_stock_data$Close, 8)
initial_stock_data$Close_L9 <- lag(initial_stock_data$Close, 9)
initial_stock_data$Close_L10 <- lag(initial_stock_data$Close, 10)
# Fill in NA entries in beginning of lag variables
initial_stock_data <- initial_stock_data %>%
fill(Close_L1, .direction = "up") %>%
fill(Close_L2, .direction = "up") %>%
fill(Close_L3, .direction = "up") %>%
fill(Close_L4, .direction = "up") %>%
fill(Close_L5, .direction = "up") %>%
fill(Close_L6, .direction = "up") %>%
fill(Close_L7, .direction = "up") %>%
fill(Close_L8, .direction = "up") %>%
fill(Close_L9, .direction = "up") %>%
fill(Close_L10, .direction = "up")
# Obtain four simple moving averages
initial_stock_data$SMA_cl_1W <- simple_moving_average(initial_stock_data$Close, 5)
initial_stock_data$SMA_cl_2W <- simple_moving_average(initial_stock_data$Close, 10)
initial_stock_data$SMA_cl_1M <- simple_moving_average(initial_stock_data$Close, 20)
initial_stock_data$SMA_cl_2M <- simple_moving_average(initial_stock_data$Close, 40)
# Compute Exponential moving average
initial_stock_data$EMA_cl_1W <- exponential_moving_average(initial_stock_data$Close, 5)
initial_stock_data$EMA_cl_2W <- exponential_moving_average(initial_stock_data$Close, 10)
initial_stock_data$EMA_cl_1M <- exponential_moving_average(initial_stock_data$Close, 20)
initial_stock_data$EMA_cl_2M <- exponential_moving_average(initial_stock_data$Close, 40)
# Compute the simple moving deviation
initial_stock_data$SMD_cl_1W <- simple_moving_deviation(initial_stock_data$Close, 5)
initial_stock_data$SMD_cl_2W <- simple_moving_deviation(initial_stock_data$Close, 10)
initial_stock_data$SMD_cl_1M <- simple_moving_deviation(initial_stock_data$Close, 20)
initial_stock_data$SMD_cl_2M <- simple_moving_deviation(initial_stock_data$Close, 40)
# Compute the MACD
initial_stock_data$MACD = (initial_stock_data$EMA_cl_2W - initial_stock_data$EMA_cl_1M)
###########################
# Begin iteratively predicting closing prices using for-loop
###########################
# Store the number of observations in our original dataframe for convenience
N <- length(date_vector)
# buffer which will keep track of Closing values to then add to the next iterations
# lag variables
buffer <- initial_stock_data$Close
for(i in 1:num_days){
# Variables that will be added to new row in dataframe
new_date <- as.Date(first_date) + i
new_L1 <- buffer[(N +i - 1)]
new_L2 <- buffer[(N +i - 2)]
new_L3 <- buffer[(N +i - 3)]
new_L4 <- buffer[(N +i - 4)]
new_L5 <- buffer[(N +i - 5)]
new_L6 <- buffer[(N +i - 6)]
new_L7 <- buffer[(N +i - 7)]
new_L8 <- buffer[(N +i - 8)]
new_L9 <- buffer[(N +i - 9)]
new_L10 <- buffer[(N +i- 10)]
new_SMA_cl_1W <- mean(buffer[(N + i-5):(N+i-1)])
new_SMD_cl_1W <- sd(buffer[(N +i -5):(N+i-1)])
new_EMA_cl_1W <- exponential_moving_average_helper(buffer[(N + i-5):(N +i -1)], 5, (1/3))
new_EMA_cl_2W <- exponential_moving_average_helper(buffer[(N + i-10):(N +i - 1)], 10, (2/11))
new_EMA_cl_1M <- exponential_moving_average_helper(buffer[(N + i-20):(N+i-1)], 20, (2/21))
new_MACD <- new_EMA_cl_2W - new_EMA_cl_1M
# Add new row to our dataframe based on previous predicted values
initial_stock_data[N + i, ] = c(0, 0, 0, 0, 0, 0, new_date,
new_L1, new_L2, new_L3, new_L4, new_L5, new_L6, new_L7, new_L8, new_L9, new_L10,
new_SMA_cl_1W, 0, 0, 0,
new_EMA_cl_1W, new_EMA_cl_2W, new_EMA_cl_1M, 0,
new_SMD_cl_1W, 0, 0, 0,
new_MACD)
# predict next row, store in buffer variable
buffer <- predict(tidy_fit, new_data = initial_stock_data %>% select(-Close))$.pred
# update the closing price of the N+i-th day to our prediction
initial_stock_data$Close[N+i] <- buffer[N+i]
}
# Finally, check value of boolean flag include.historical to see if we want
# ALL the closing prices (including data pulled) or just values we predicted
if(include.historical == TRUE){
return(initial_stock_data$Close)
}
else{
return(initial_stock_data$Close[(N+1) : (N+num_days)])
}
}We are then able to pass every model fit (i.e the objects returned
from the fit() function applied to our workflows) into the
new function to get a 15-day forecast for each of our stocks. It is
worth noting that we also include a include.historical
boolean flag to allow us to plot previous stock data before the
forecasting window — in particular, we will use this to show where our
models begin diverging from the actual stock price.
beginning_forecast_date <- as.Date("2024-01-01")
beginning_actual_date <- as.Date("2023-12-01")
ending_actual_date <- as.Date("2024-01-24")
# Create new data frames so that we are not overriding our original train/test data
AMD2 <- loadSymbols("AMD", from=beginning_actual_date, to=ending_actual_date, auto.assign = FALSE)
NVDA2 <- loadSymbols("NVDA", from=beginning_actual_date, to=ending_actual_date, auto.assign = FALSE)
INTC2 <- loadSymbols("INTC", from=beginning_actual_date, to=ending_actual_date, auto.assign = FALSE)
TSM2 <- loadSymbols("TSM", from=beginning_actual_date, to=ending_actual_date, auto.assign = FALSE)
my_dates2 <- index(AMD2)
# Typecast everything as dataframe
AMD2 <- data.frame(AMD2)
NVDA2 <- data.frame(NVDA2)
INTC2 <- data.frame(INTC2)
TSM2 <- data.frame(TSM2)
# Add dates for the horizontal axis of our graphs
AMD2$Date <- my_dates2
NVDA2$Date <- my_dates2
INTC2$Date <- my_dates2
TSM2$Date <- my_dates2
# Obtain linear regression forecasts
AMD2$Linear_Pred <- forecast_comparison("AMD", linear_fit_AMD, beginning_forecast_date, num_days=15, include.historical=TRUE)
NVDA2$Linear_Pred <- forecast_comparison("NVDA", linear_fit_NVDA, beginning_forecast_date, num_days=15, include.historical=TRUE)
INTC2$Linear_Pred <- forecast_comparison("INTC", linear_fit_INTC, beginning_forecast_date, num_days=15, include.historical=TRUE)
TSM2$Linear_Pred <- forecast_comparison("TSM", linear_fit_TSM, beginning_forecast_date, num_days=15, include.historical=TRUE)
# Obtain Elastic Net forecasts
AMD2$Elastic_Net_Pred <- forecast_comparison("AMD", elastic_net_fit_AMD, beginning_forecast_date, num_days=15, include.historical=TRUE)
NVDA2$Elastic_Net_Pred <- forecast_comparison("NVDA", elastic_net_fit_NVDA, beginning_forecast_date, num_days=15, include.historical=TRUE)
INTC2$Elastic_Net_Pred <- forecast_comparison("INTC", elastic_net_fit_INTC, beginning_forecast_date, num_days=15, include.historical=TRUE)
TSM2$Elastic_Net_Pred <- forecast_comparison("TSM", elastic_net_fit_TSM, beginning_forecast_date, num_days=15, include.historical=TRUE)
# Obtain KNN forecasts
AMD2$KNN_Pred <- forecast_comparison("AMD", knn_fit_AMD, beginning_forecast_date, num_days=15, include.historical=TRUE)
NVDA2$KNN_Pred <- forecast_comparison("NVDA", knn_fit_NVDA, beginning_forecast_date, num_days=15, include.historical=TRUE)
INTC2$KNN_Pred <- forecast_comparison("INTC", knn_fit_INTC, beginning_forecast_date, num_days=15, include.historical=TRUE)
TSM2$KNN_Pred <- forecast_comparison("TSM", knn_fit_TSM, beginning_forecast_date, num_days=15, include.historical=TRUE)
# Obtain Random forest forecasts
AMD2$Random_Forest_Pred <- forecast_comparison("AMD", random_forest_fit_AMD, beginning_forecast_date, num_days=15, include.historical=TRUE)
NVDA2$Random_Forest_Pred <- forecast_comparison("NVDA", random_forest_fit_NVDA, beginning_forecast_date, num_days=15, include.historical=TRUE)
INTC2$Random_Forest_Pred <- forecast_comparison("INTC", random_forest_fit_INTC, beginning_forecast_date, num_days=15, include.historical=TRUE)
TSM2$Random_Forest_Pred <- forecast_comparison("TSM", random_forest_fit_TSM, beginning_forecast_date, num_days=15, include.historical=TRUE)
# Obtain boosted tree forecasts
AMD2$Boosted_Trees_Pred <- forecast_comparison("AMD", boosted_trees_fit_AMD, beginning_forecast_date, num_days=15, include.historical=TRUE)
NVDA2$Boosted_Trees_Pred <- forecast_comparison("NVDA", boosted_trees_fit_NVDA, beginning_forecast_date, num_days=15, include.historical=TRUE)
INTC2$Boosted_Trees_Pred <- forecast_comparison("INTC", boosted_trees_fit_INTC, beginning_forecast_date, num_days=15, include.historical=TRUE)
TSM2$Boosted_Trees_Pred <- forecast_comparison("TSM", boosted_trees_fit_TSM, beginning_forecast_date, num_days=15, include.historical=TRUE)Plotting the forecasts associated to the different models across each of stocks, we begin to notice new characteristics of our models that may be harder to identify for problems involving non-longitudinal data (i.e. non-time series problems such as classification). For example, we notice that the random forest forecast for each of the four stocks has a tendency to stabilize towards a constant value. Similarly, the linear regression algorithms have a tendency to follow the trend predicted on the first day of the forecast (which is somewhat expected, as we are fitting the data with ).
# plot different outcomes for AMD
g1<-ggplot(data = AMD2, aes(x=Date)) +
geom_line(aes(y = Linear_Pred, color = 'Linear Regression Prediction')) +
geom_line(aes(y = Elastic_Net_Pred, color = 'Elastic Net Prediction')) +
geom_line(aes(y = KNN_Pred, color = 'k-Nearest Neighbors Prediction')) +
geom_line(aes(y = Boosted_Trees_Pred, color = 'Boosted-Trees Prediction')) +
geom_line(aes(y = Random_Forest_Pred, color = 'Random Forest Prediction')) +
geom_line(aes(y = AMD.Close, color = 'Actual Closing Value')) +
ylab('USD') +
scale_color_manual(values = c(
'Boosted-Trees Prediction' = 'coral1',
'k-Nearest Neighbors Prediction' = 'brown1',
'Elastic Net Prediction' = 'burlywood1',
'Random Forest Prediction' = 'deeppink',
'Actual Closing Value' = 'green',
'Linear Regression Prediction' = 'gold'
)) +
xlab('Date') +
ggtitle("Forecast of AMD Close Price") +
scale_y_continuous(labels = label_comma()) +
theme_dark()
# plot different outcomes for NVDA
g2<-ggplot(data = NVDA2, aes(x=Date)) +
geom_line(aes(y = Linear_Pred, color = 'Linear Regression Prediction')) +
geom_line(aes(y = Elastic_Net_Pred, color = 'Elastic Net Prediction')) +
geom_line(aes(y = KNN_Pred, color = 'k-Nearest Neighbors Prediction')) +
geom_line(aes(y = Boosted_Trees_Pred, color = 'Boosted-Trees Prediction')) +
geom_line(aes(y = Random_Forest_Pred, color = 'Random Forest Prediction')) +
geom_line(aes(y = NVDA.Close, color = 'Actual Closing Value')) +
ylab('USD') +
scale_color_manual(values = c(
'Boosted-Trees Prediction' = 'coral1',
'k-Nearest Neighbors Prediction' = 'brown1',
'Elastic Net Prediction' = 'burlywood1',
'Random Forest Prediction' = 'deeppink',
'Actual Closing Value' = 'green',
'Linear Regression Prediction' = 'gold'
)) +
xlab('Date') +
ggtitle("Forecast of NVDA Close Price") +
scale_y_continuous(labels = label_comma()) +
theme_dark()
# plot different outcomes for INTC
g3<-ggplot(data = INTC2, aes(x=Date)) +
geom_line(aes(y = Linear_Pred, color = 'Linear Regression Prediction')) +
geom_line(aes(y = Elastic_Net_Pred, color = 'Elastic Net Prediction')) +
geom_line(aes(y = KNN_Pred, color = 'k-Nearest Neighbors Prediction')) +
geom_line(aes(y = Boosted_Trees_Pred, color = 'Boosted-Trees Prediction')) +
geom_line(aes(y = Random_Forest_Pred, color = 'Random Forest Prediction')) +
geom_line(aes(y = INTC.Close, color = 'Actual Closing Value')) +
ylab('USD') +
scale_color_manual(values = c(
'Boosted-Trees Prediction' = 'coral1',
'k-Nearest Neighbors Prediction' = 'brown1',
'Elastic Net Prediction' = 'burlywood1',
'Random Forest Prediction' = 'deeppink',
'Actual Closing Value' = 'green',
'Linear Regression Prediction' = 'gold'
)) +
xlab('Date') +
ggtitle("Forecast of INTC Close Price") +
scale_y_continuous(labels = label_comma()) +
theme_dark()
# plot different outcomes for TSM
g4<-ggplot(data = TSM2, aes(x=Date)) +
geom_line(aes(y = Linear_Pred, color = 'Linear Regression Prediction')) +
geom_line(aes(y = Elastic_Net_Pred, color = 'Elastic Net Prediction')) +
geom_line(aes(y = KNN_Pred, color = 'k-Nearest Neighbors Prediction')) +
geom_line(aes(y = Boosted_Trees_Pred, color = 'Boosted-Trees Prediction')) +
geom_line(aes(y = Random_Forest_Pred, color = 'Random Forest Prediction')) +
geom_line(aes(y = TSM.Close, color = 'Actual Closing Value')) +
ylab('USD') +
scale_color_manual(values = c(
'Boosted-Trees Prediction' = 'coral1',
'k-Nearest Neighbors Prediction' = 'brown1',
'Elastic Net Prediction' = 'burlywood1',
'Random Forest Prediction' = 'deeppink',
'Actual Closing Value' = 'green',
'Linear Regression Prediction' = 'gold'
)) +
xlab('Date') +
ggtitle("Forecast of TSM Close Price") +
scale_y_continuous(labels = label_comma()) +
theme_dark()
wrapper <- swipeRwrapper(
tags$div(style = "width: 700px; margin: auto;", ggplotly(g1)),
tags$div(style = "width: 700px; margin: auto;", ggplotly(g2)),
tags$div(style = "width: 700px; margin: auto;", ggplotly(g3)),
tags$div(style = "width: 700px; margin: auto;", ggplotly(g4))
)
swipeR(wrapper, height = "400px", navigationColor = "navy")The area between the forecast’s curve and the actual outcome (with
respect to some measure \(\mu\) on the
parameter space) provides a variety of new ways to test the accuracy of
models’ long term behavior. Unfortunately, constructing such a metric
that is compatible with the yardsticks package (and thus
can be used with a grid_regular to tune hyperparameters) is
outside the scope of this project and would likely require several
methods above to be heavily optimized. As a consequence, we
Conclusion
In a certain sense, stock market data is one of the most difficult things to completely predict — societal influences, supply chain issues, and even international politics can cast ripples which are difficult for any model to predict. On the other hand, our analysis shows that Occam’s razor holds its merit even within the discipline of statistical learning: the model that is simplest to describe will often be the correct fit. After exploring a bit of the theory surrounding time-series, however, this outcome is not entirely unexpected: one of the most popular models for predicting stock-market behavior is known as ARIMA (Auto-Regression Integrated Moving Average). As the name suggests, this is a well-tuned regression model trained on its lag variables and moving averages, together with an “integrating” / differencing step which handles seasonal or non-stationary data. Ultimately, seasonality and trends in the data were not factors taken into account for this analysis, and thus will likely
- mention RSI
- mention prophet model, seasonality, and events
- mention metric for long-term prediction
- mention finding optimal lag distance